{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import datetime as dt\n",
    "import numpy as np\n",
    "import os\n",
    "import gc\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as  mpl\n",
    "from matplotlib  import pyplot as plt\n",
    "mpl.rcParams[u'font.sans-serif'] = ['simhei']\n",
    "mpl.rcParams['axes.unicode_minus'] = False\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "import joblib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 读取优化测试\n",
    "joblib+lz4表现比较好和稳定"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2min 25s, sys: 7.08 s, total: 2min 32s\n",
      "Wall time: 2min 33s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "data_raw = pd.read_csv('./preprocess_data_new/train_ax_nodup.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['./preprocess_data_new/train_ax_nodup.joblib']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import joblib\n",
    "joblib.dump(data_raw,'./preprocess_data_new/train_ax_nodup.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.84 s, sys: 5.09 s, total: 6.93 s\n",
      "Wall time: 1min 44s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "data_raw = joblib.load('./preprocess_data_new/train_ax_nodup.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2min 9s, sys: 1.72 s, total: 2min 11s\n",
      "Wall time: 2min 14s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['./preprocess_data_new/train_ax_nodup.zlib']"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "joblib.dump(data_raw, './preprocess_data_new/train_ax_nodup.zlib', compress='zlib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 16.7 s, sys: 3.86 s, total: 20.5 s\n",
      "Wall time: 30 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['./preprocess_data_new/train_ax_nodup.lz4']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "joblib.dump(data_raw, './preprocess_data_new/train_ax_nodup.lz4', compress='lz4')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 多进程的方式读取csv，失败\n",
    "# %%time\n",
    "# data_len = 100000\n",
    "# from joblib import Parallel, delayed\n",
    "# def load_col(skiprows):\n",
    "#     tmp_df = pd.read_csv('./preprocess_data_new/train_ax_nodup.csv',header=None,skiprows=skiprows,nrows=2000)\n",
    "#     return tmp_df\n",
    "# def load_parallel():\n",
    "#     result = Parallel(n_jobs=32,verbose=10)(delayed(load_col)(skiprows) for skiprows in range(0,data_len,2000))\n",
    "#     return result\n",
    "# result = load_parallel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 11.4 s, sys: 41.5 s, total: 52.9 s\n",
      "Wall time: 53 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "data_raw_new = joblib.load('./preprocess_data_new/train_ax_nodup.lz4')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 16.3 s, sys: 3.88 s, total: 20.2 s\n",
      "Wall time: 20.2 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "data_raw = joblib.load('./preprocess_data_new/train_ax_nodup.lz4')\n",
    "data_raw = data_raw.drop(columns=['id','loan_dt','tag'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3.32 s, sys: 1.02 s, total: 4.34 s\n",
      "Wall time: 4.35 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "data_test = joblib.load( './preprocess_data_new/valid_nodup.lz4')\n",
    "data_test = data_test.drop(columns=['id','loan_dt'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 查看时间"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### day_val_df for train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 加载train的数据\n",
    "data_date = pd.read_csv('./preprocess_data/train_x_date.csv').drop(columns=['id','loan_hour'])\n",
    "data_label = pd.read_csv('./preprocess_data/train_y_33465.csv',usecols=['label'])\n",
    "data_loan_dt = pd.read_csv('./preprocess_data_new/train_ax_nodup.csv',usecols=['loan_dt'])\n",
    "data_loan_dt.loan_dt = pd.to_datetime(data_loan_dt.loan_dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4    0.358608\n",
       "3    0.358050\n",
       "2    0.202856\n",
       "1    0.175185\n",
       "5    0.003775\n",
       "Name: loan_month, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 各个月份样本数分布\n",
    "data_date.loan_month.value_counts()/30465"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gen_day_val(data_date,data_label,data_loan_dt):\n",
    "    df1 = pd.concat([data_date,data_label],axis=1)\n",
    "    plt.title('loan_day and label_ratio')\n",
    "    df1.groupby(by=['loan_day']).mean().label.plot()\n",
    "    plt.hlines(data_label.label.mean(),0,31,colors='r')\n",
    "    \n",
    "    day_val_df = pd.DataFrame()\n",
    "    day_val_df['day_val'] = np.zeros(data_date.shape[0])\n",
    "    \n",
    "    for day in range(1,32):\n",
    "        day_val_df.loc[data_date.loan_day==day,'day_val'] = df1.groupby(by=['loan_day']).mean().label.loc[day]\n",
    "\n",
    "    day_val_df['day_val_state'] = 0\n",
    "    day_val_df.loc[day_val_df.day_val>data_label.label.mean(),'day_val_state'] = 1\n",
    "    return day_val_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "day_val_df = gen_day_val(data_date,data_label,data_loan_dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['./preprocess_data_discrete/day_val_df.lz4']"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(day_val_df,'./preprocess_data_discrete/day_val_df.lz4',compress='lz4')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### week_df for train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gen_week(data_loan_dt,data_label):\n",
    "    \n",
    "    week_df = pd.DataFrame()\n",
    "    week_df['loan_week_day'] = data_loan_dt.loan_dt.apply(lambda x: x.weekday()+1)\n",
    "    \n",
    "    # 逾期率随时间起伏变大\n",
    "    import matplotlib.pyplot as plt\n",
    "    df3 = pd.concat([week_df[['loan_week_day']],data_label],axis=1)\n",
    "    df3.groupby(by=['loan_week_day']).mean().plot()\n",
    "    plt.hlines(data_label.label.mean(),0,7,colors='r')\n",
    "    plt.title('variance becomes bigger with week_day')\n",
    "    \n",
    "    week_df['week_day_val'] = 0\n",
    "    for i in range(1,8):\n",
    "        week_df.loc[week_df['loan_week_day']==i,'week_day_val'] = df3.groupby(by=['loan_week_day']).mean().label.iloc[i-1]\n",
    "\n",
    "    week_df['week_day_state'] = 0\n",
    "    week_df.loc[week_df['week_day_val']>data_label.label.mean(),'week_day_state'] = 1\n",
    "    return week_df\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "week_df = gen_week(data_loan_dt,data_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['./preprocess_data_discrete/week_df.lz4']"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(week_df,'./preprocess_data_discrete/week_df.lz4',compress='lz4')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### day_val_df for test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### week_df for test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "valid_date = pd.read_csv('./preprocess_data/valid_date.csv').drop(columns=['id','loan_hour'])\n",
    "valid_loan_dt = pd.read_csv('./preprocess_data_new/valid_nodup.csv',usecols=['loan_dt'])\n",
    "valid_loan_dt.loan_dt = pd.to_datetime(valid_loan_dt.loan_dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4    0.32795\n",
       "3    0.30270\n",
       "1    0.18770\n",
       "2    0.17815\n",
       "5    0.00350\n",
       "Name: loan_month, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看各个月份样本数的分布\n",
    "valid_date.loan_month.value_counts()/20000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "day_val_test = gen_day_val(valid_date,data_label,valid_loan_dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "week_test = gen_week(valid_loan_dt,data_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['./preprocess_data_discrete/week_test.lz4']"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(day_val_test,'./preprocess_data_discrete/day_val_test.lz4',compress='lz4')\n",
    "joblib.dump(week_test,'./preprocess_data_discrete/week_test.lz4',compress='lz4')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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SMyN9hhKxKyj4g/5DrF0hhAiKtcCmY5uoaK7g6ilX90lnFGgL0Jv1tHW1BfT+QzUnueLC4gtRCzV3zb9ryM3OQJCnyQuqsJfoHI6O2iit1++dlT6Lw4bDtHa2BmBlI4Mi7AohgcHi3k6gN86Sx5GsWni59GXSY9M5p/CcPscLtI4N1EBvEjp9YjyN2AEW5yxmw2UbWJi9MFDLGpR8bT5VLVVBqS6RUrJHv8frNIyTmekzkcieAdijEUXYFUKCwewEelOcVIypy8Tx9uMjsCo4bDjM5mObuXzS5X1G0sGJTcJAV8Z4m4pxMpj/eKDJ0+TRbm2nydI04vc+1nbM4ejoYf16f6anT0cgRnWeXRF2hZDA0GEYMDnJFcVJDreKkUrHvLL/FWLUMfxwwg8HvObckBwJYY8QEUOmqkKJPE0eQFDSMSW6bkdHLytinGiiNBQnF4/qRiVF2BVCAoPFtRd7f5zCPhIbqI3mRt4vf5+V41a6/NCJjYhlTNyYgG+g6sw6UmJTgtrF6S1BFXZ9CdHq6B7jOF+YlT6L3brdo9aPfvT8pCiELR22Dtqt7R6lYpJikkiPTeewIfAlj6sPrqbT3smVU650e06BtiDgEburIdahTk5CDiqhCkote4m+hMkpnjk6umNm+kxau1qD7k3kK4qwKwSdnhp2D1Ix4NvQDV27zquNvA5bB68ffJ2luUsZmzjW7XnOTcJA4mqIdagTpY4iMy5zxCP2LnsXpY2lPqdhnIz2RiVF2BWCTo+dQPTQETs4PGPKjeUePyZ/fPRjTltzGtd+dC0Hmzzzp1tbsZYmSxNXTblq0PMKNAUYOgwejaTzFU/tBEKNPG0e1S0jK+yHDYfpsHX4vHHqJF+TT3J0MrsaFGFXUPAJT+wEelOcVIzFZqHWVDvkueXGcu7ZdA/FScUcaT7CJe9fwkNbH6Kls8Xte6SUvFT6EhOSJzA/c/6g1w+0L4rVbqXJ0uSxnUAoka8ZmQau3jgbk6alTRvWdYQQzMyYyR7d6Cx5VIRdIej4koqBoStjWjtb+Z8v/4fYiFj+dca/eO/C9/jhhB/y2oHXOO/t83in7B2XUf/XdV9TZizjqilXDemvUqgtBAI3dKPJ0oREkhYzCiN2TR7GDuOgH6L+Zo/O4eiYk5Az7GvNSp/F0ZajPT+fowlF2BWCjrepGKcZ2GDCLqXknk33UG2q5rFljzEmfgyJ0YncveBuXv/+6+Rp8rhn0z1c/eHV7G/c3+e9L5e+TGpMKueOPXfIteRqch2bhAGK2Htq2ONGn7A76/xHMs9eoi9hetp0vxiezUx3DN54qfQlrHbrsK83kijCrhB0DB0GVEKFNtqz9u/4yHhyEnIGNQN7ft/zfFb1Gb8++dcDRrRNSZ3CS997iQcWP0C1qZrLPriMB7c8SHNHMxXGCjbWbuSySZd51IofpY4iKz6Loy1HPVq7t/hiJxAq5GpygZETdlOnyeHo6GPHaX9mZczizIIzeabkGa5ae9WIVGL5C0XYFYKO007AmzrtwSpjttRt4YkdT3B24dlcPeVql+eohIoLii/gvQvf47KJl7Hm0BrOe/s87tt8H9HqaC6ZeInHa8nXBK4yxhc7gVChp5Z9hDZQ9+r3IpF+E/YIVQR/WfYXHl36KMfajnHJ+5fw1K6n6LJ1+eX6gUQRdoWgY+wwetSc1JtxSeM40nyELnvfX7L6tnpuW3cbhdpC/rDoD0M+kmujtPxu/u/4z/f/Q2FiIbt0u/h+0fc9qql3UqAtCJgviq92AqFAXGQcabFpIxaxOyuevHV0HAwhBOeMPYd3zn+HswrO4p+7/8mlH1zas0kbqijCrhB0mixNHm+cOilOKsZqt/aJlDttnfzvV/9Lp72T/zv1/wa1AO7PpJRJvHjOizx91tP8du5vvVpLgbYAU5epp7rHn+jMOhKjE4Pi0OgPRrIypspURXJ0MonRiX6/dnJMMo8sfYS/nfY3mjuauWLtFTy+/XEsVovf7+UPFGFXCDqeGoD1xtku3jsd8/C2hynRl/DHxX8ctKnIHUIIFmQt8HouaCA9Y/Rmz2adhiq5mpHzZa8yVfWkfwLF8rzlvHP+O1xYfCHP73uei9+9mO312wN6T1/wWNiFEP8UQpzX/fdnhRCbhRB393p9wDEFBU8wdhg9suztzdjEsaiEqkfY3z78NmsOreH6addzesHpgVimW5z2vYEQdp1ZR2psqt+vO1Lka/JpaG/AbDUH/F41phrytIEVdnCYhP1+0e955qxnsEkb1318HQ9ueZD2rvaA39tTPBJ2IcQpQKaU8j0hxEWAWkq5CMgWQox3dSyAa1YII+zS7sixe+lcGK2OJl+TT5mhjNLGUh7c8iDzs+bzi9m/CNBK3ZOdkE2EiAjIBupotBPojfNppsZUE9D7dNm6qGurC3jE3pv5WfN5a+VbXDn5SlYfXM0ftvxhxO49FEMKuxAiEngaOCqEOB9YDqzufvkLYImbYwoKQ9LS0YJd2r1OxYAjz763cS//+9X/khKbwp+X/pkIVUQAVjk4kapIcjQ5fo/YpZTo2nWjWthHyuWxtrUWu7T31M6PFHGRcdw+73ZunH4jH1R8wM6GnSN6f3d4ErFfDZQCfwbmATcDzl7uFmAMEO/iWB+EED8WQmwXQmzX6XTDXbdCmNDU4RjE4G0qBhyeMfVt9TS0N/D4ssd9+nDwF4HYJGzpbKHT3jkqK2KcjJSwO7/3Ixmx9+bG6TeSEZfBQ1sfwma3BWUNvfFE2GcDq6SU9cArwHogtvu1hO5rtLo41gcp5Sop5Rwp5Zz09NEbgSj4F2fXqS9DJKakTAHgjnl3DNvNb7g47Xv9WfLYaG4ERmepo5PE6ES0UdqAC7vz+sES9rjIOG49+Vb2N+3nnbJ3grKG3ngi7GVAUfff5wCFnEi1zASOAt+6OKagMCRGixHAp2h7Wd4y3jn/Ha+aiQJFvjYfs9WMzuy/p1HntUajAVhvAtnA5aTaVE1cRFxQn9q+N/Z7nJRxEk/seGJE/XFc4YmwPwucKoRYD/wcRz79KiHE48AlwAfAOy6OKSgMyXBSMSqh6vGNCTaBqIxxCvtojtjBEUUHPBXTUkW+Nt8vHjG+IoTgjnl3YOww8tSup4K2DvBA2KWUJinlD6WUS6WUC6WUlTjEfQtwqpSyWUrZ0v9YIBetED4MJxUTSjiF3Z+Rqb599Had9iZPm0ddW92ALmF/Um2qDloapjeTUydz8YSLee3Aa0GdvuRTg5KU0iClXN2dd3d7TEFhKAwWA3ERcUSro4O9lGGRGZdJpCrSrxG73qwnRh1DQmSC364ZDPI0edikjbrWuoBc32a3UdNaExLCDvCL2b8gLjKOh7c9HBCbCU9QOk8VgoqhwzDqo3UAtUpNnibP76mYtNi0oKYX/IGzBDFQ1gLH249jtVtHvNTRHSkxKdw862a21G3hi+ovgrIGRdgVgorRYgzqhpc/KdAW+FW89Gb9qN84hcCXPAa71NEVl0y8hOKkYh795lE6bB0jfn9F2BWCSpOlyaeN01DE6fLo6SzWodCb9aM+vw6OPYLYiNiAVcY4PzCcXa6hQKQqktvn3U5tay0v7ntxxO+vCLtCUAmXVAw4hKXT3kl9m3+2mZypmNGOECKglTHVLdVEqaLIiMsIyPV9ZUHWAs7IP4NnSp7x28+EpyjCrhBUwioVo/FfyaPFasHUaRrVdgK9Caiwm6p7RhSGGrfOuRWb3cbj3z4+ovcNve+EwneG9q52LDZL2KRinKkAf6QcRvOADVfka/KpMdX4LU3Vm5Gw6/WVXE0u1027jg+PfMiO4ztG7L6KsCsEDedginCJ2DPiMoiNiKXSNPyIPdyEPVeTS6e9k4b2Br9eV0oZMjXs7rhh+g1kxmfy0LaR85FRhF0haDjtBMIlYlcJFXmaPL9G7OFQFQP+fZrpTaOlEbPVHNLCHhsRy60n38qBpgO8VfbWiNxTEfbvGFa7lfu/vp8Hvn6ANYfWUKIrCdp4ryaLw04gXDZP4YQZ2HAJFzsBJ4EqeXR+UIRSRYwrzi48m5PHnMyTO56kuSPwjfkjb16tEFQ+r/qcNw69QWxELKsPOSz01ULN2MSxTEqZ1OdPIGZH9sbY4bsBWKiSr8nny6ovsdqtw/KG15v1qITK6yHfoUpmXCYRqgi/NykF29XRU4QQ/G7e77jk/Ut4avdT3DHvjoDeTxH2INFl66K+vX5EfyCllLy07yXyNHm8e8G71LXVcbDpIPub9nOg6QDb6rbxfsX7PednxWdxy+xbWDluZUDW44zYvR1kHcoUaAuwSit1rXXDGtOmN+tJjUlFrVL7cXXBQ61Sk5vg//mnVaYq1EJNdny2X68bCCamTOSm6TeNyNOFIuxB4tUDr/LY9seYM2YO1027jlNyTgl46/hu3W726Pdw5/w7iVBFkKfJI0+TxxkFZ/Sc02hu7BH7Tys/5f7N9zM1dWpAXBQNFgMRIgJNpMbv1w4WPS6PpsphCbuuPTxq2HsTiJLHalM1WfFZRKoj/XrdQHHL7FtG5D5Kjj1I7NXvRROlodpUzc2f38xF717Eu+XvBtQB78V9L6KN0nL+uPPdnpMam8qinEXcMP0G/n7634mLjOOujXcFZF3GDiNJMUmj3gulN85obLh59nCxE+hNvtbhy+5PY6zqltCuiAkWirAHifLmck7KOIkPL/6QPy35EwB3bbyLc986l5f2vURbV5tf71fdUs3nVZ9z6cRLiYuM8+g9abFp3LPgHvY17uOZkmf8uh5wpGJcbZzqWzv4aO/oNAlNjUklPjLeL8IejhF7u7W9JwXnD6pbq0N+4zQYKMIeBKx2K0ebjzIuaRyRqkjOG3ceb618i3+c/g9yE3J5dPujnPnGmTy548mesrfh8vL+l1Gr1Fw26TKv3ndW4VmcO/ZcVu1eRWljqV/W4sTYYSQleuDG6b+3VPHTV76lxtDu1/uNBEKIHs8YX7HZbTRaGsNS2MF/lTHNHc00dzQrEbsLFGEPAtWmarrsXX3y1kIIluYu5flznuff5/6bBVkLeKbkGc5+42z+uOWPdNo6fb5fc0cz75S9w7ljz/XJT+PO+XeSHJPMXRvv8qtTncFicLlxWt0t6F+XN/rtXiNJgWZ4JY+GDgN2aQ8bOwEn/hb2GlNNn+sqnEAR9iDgnKzibkNyRvoMHl/+OO9e8C7njTuP1w++zr92/8vn+605tAaz1czVU6726f2J0Yncv+h+yoxl/GPXP3xeR3+aLE0uy/lqDWYAvq4YncKer83nWNsxumy+7Uvo2t3XsBvbO/m09Piw1hcschJyUAmV30oefbXr1Zk6+M2a3TSbA7efFWwUYQ8CTmEfqx076HmFiYX8ftHvWTluJc/tfY79jfu9vleXrYtX97/KwqyFTEyZ6NN6AU7JPYWLx1/MC3tfYGfDTp+v48Rqt9LS2eKyhr3G6IjYt5Q3Bm0CzXAo0BZgl3aqW32LTAezE/j31ipuemk72474L089UkSpo8iMy/RbxO5Md+Vqcr1633931fLGtzV8tDcwE51CAUXYg0C5sZychByPNzFvm3sbyTHJ3LPpHq+rU9YeWYvOrOOaqdf4stQ+/Hbub8lOyOaujXfR3jW8/LezOal/KsZml9QZLaTER3Gs2UJV0+jLsw+3fX4wO4Gjesem+j+/KvNxdcElT5tHdYt/hL3aVE1GrMOfxxvWHXI8EX11UOeXdYQigwq7ECJCCFElhPiq+890IcT9QohvhBB/73XegGMK7ilvLveqLjwxOpG7F9zNQcNBnit5zuP3SSl5sfRFipOKWZS9yJel9iE+Mp4HFj9Ajalm2Dak7oZYN5gsWO2S82c5Gk5GY569UFsI+F7yOJidQGX3B91XB3XsrR19M+P9Wctebar2ulfA3Glj65EmVAI2HtbTZfO/22QoMFTEPgN4TUq5XEq5HIgGlgDzgBohxBlCiDn9jwVywaOdnoqYRO8afk7PP51zCs/hX3v+RZnBs2jt67qvOWw4zNVTrvZbrfjczLlcOeVK/nPwP2w+ttnn6zgj9v45dmd+femEdNI10aMyz54YnUhidOKwInZNlMblgO+qxnbOnDIGTXTEqIza8zX5GDoMmDpNw76WL64S9K+sAAAgAElEQVSOW4400mm1c/m8fEwdVnZUGoa9jlBkKGFfAFwohNgohPg3cBrwpnQkPj8DTgGWujg2ACHEj4UQ24UQ23W68H0EGooaUw2d9k6fOjl/N/93aCI13LPpHqx265Dnv1T6EqkxqawoWuHLUt3yy9m/ZGziWO7ddC8tnS0+XcOdAVit0SHsecmxLChK5evRmmcfRmWM3qx3WRFj6bJR32JhWnYiVy8q4MO99ZQ1tA53qSOKvypj2rva0Zl1Xg+wXndQR0ykil+fOYEIleCrQ+GpRUMJ+zfAMinlEsAIxAK13a+1AGOAeBfHBiClXCWlnCOlnJOeHl5lXN5Q3jx4RcxgpMSkcOf8O9nbuJeXS18e9NwyQxmbajdx+aTLiVJH+bRWd8RExPDHxX9Eb9bzyLZHfLqG07K3/+ZpTXfEnp0Uy8KiVBpMHVTo/dusNRLka/N99mXXtetcCruzrr8gNY7rF48lOkLFU1+VD2udI41T2IdbGVPT2l3q6GUqZv0hHQuKUklLiObkguSwzbMPJex7pJTOreMDQCsOcQdI6H6/q2MKbnBWxBQlFvn0/rMLz+a0vNP4+86/c6T5iNvzXip9iRh1DJdOvNSn+wzF9PTp3DD9Bt4tf5cvqr7w+v1NHY6Ivb+DZI3BTEp8FHFRESwclwqMzjx7gbaA+rZ6nyyRdWYdaXEu8uuNDmHPT40jNSGay+fl886uWqqHscHcYbXxwqYjNLb6rz9hMHoi9mFuoDrf700qpqqxnQp9G8smOD40l0/MYH9dC8dbgmNbHUiGEuGXhRAzhRBq4EIc0fmS7tdmAkeBb10cU3BDubGc7Phsjyti+iOE4O4FdxMdEc19m+9zOWpMb9bzfsX7nF98fkCdE38646dMSpnE/V/f73WbuMFiQBOlIVLV17yp1mgmJ8kRJxSmxpGpjRmVeXanGZi3KQcpJY3mRtJiBhH2FMfPzk2nFKES8PSGCp/X+ZdPDvH790r5zZrdI5LyiouMIy02bdipGF/setcddkTnJ4Td8d91YRi1DyXsfwBeBnYBXwMPArOFEE8AdwCvARtdHFNwQ7nRu4oYV6THpXP73NvZ2bCT1w4M/Ha/duA1rHYrV025alj3GYpIdSR/XPJHTJ0mHt/uXZWMuyHWtYZ2cpMdwi6EYOG4VLZWjL48u69mYK1drVhsFpeljlVN7cRHqUmNd6TWspNiuWh2Lq9/U02Dyfuoc1OZnlXrKxiXHs+XB3Ws+bbG62v4Qr4mf9ipmCpTFUnRSWijtB6/Z91BHXkpsYxNiwdgUqaGTG0MXx3y77i+UGBQYZdS7pVSzpBSTpdS3iWltANnABuA70kpj7g6Fvhlj05sdhtHmo/4xQJ35biVLMlZwhM7nugT/ZitZlYfXM3yvOU9UWMgmZA8gZXjVvJ51ede1dg3dTQNGIknpewTsQMsKEpB39rJ4VG2SVig6bbv9VLYByt1rGpqJz81vk+F00+Xj8Nqs/PsRu9+7Yztndy6ejdF6fG8e8sS5o1N4YH3SqlrNnt1HV/I1Qzfl73aVO3Vxmmn1c7mcj3LJqT3fP+EECybkM6Gw3qsYVb26HU+XEppllK+IaWsGOyYwkBqWh0VMb7m13sjhOC+hfehEip+v/n3PRHtu2XvYuww+qUhyVOW5CyhtauVEl2Jx+8xWowDKmKa2jqxdNnJST4h7AuLHAI32vLsCVEJpMakeh2ZNpodX6fLGvbGNgpS+qbwxqbFs2JGNq98XUlzu2cfrFJK7ny7BH1rB09cOpv46Age/cEMrHbJHW+WBPzpKF+TT0N7w7BGMlabqr3qON1e2UR7p41lE/p6JS2fmI7JYmVHldHntYQiykbnCOLcOC1OKvbL9TLjM7l1zq1sq9/GG4ffwC7tvLz/ZaalTuOkjJP8cg9PmJ81H7VQs7F2o8fvMVgMA2rYnRUxvSP2vJRYcpJiR52wg2/zT50+Mf2rYux2SbXBTH7qwL2Zny8fR1unjRc2H/XoHm98W8Paknr+96wJTM91bF4XpMZzx/cmse6QjjXbA5uScebFnSZe3tJl66Kurc4ru951h3REqkXPhryTxePTHGWPB8MrHaMI+whS0ex4oClKGn7E7uQH43/A/Mz5/GX7X3j9wOtUtlRyzdRrRnR4hSZKw4z0GR43LEkpaeoY6MXurGHvHbELIVhQlMqWI43Y7aMvz+5zKqZfVUx9i4VOq71n47Q3k7O0nDE5g+c3H6GtY/D+hsrGNn7/7j7mjU3hJ0v7pgSvWlDAgqIUHni/lGPGwKVkeiwXfMyz17bWYpd2rzZO1x/SM6cghYTovkPjtDGRnBSGZY+KsI8gZcYysuKziI+M99s1hRDct8hRHfPQtofIis/qM+pupFicvZjSxlKPqmPautqw2q0DNk+dXae5yX3Fa+G4VIztXRyoH3634khSoC1Ab9Z7NTRFb9YTrY4eMC7Q6ZlT4CJiB/j5qcUY27t4bZt7sbTa7Pz6P7tQqQR/vXQWalXfD3+VSvDni2dik5I73gpcSma4TUrODwRPc+zHWyzsr2th2UTX/TPLJ6ZTWtdCQxiVPSrCPoJUGCv8Gq07ydPk8auTfgXAFZOvIEI18qNsF+csRiL5+tjXQ57r9Inpv3laazSjiY4gMbZvCWRPPXuIlD3a7JJbV+9mzfbBhckpPN5YCzgnJ/V/4qrqLnUsSHEdFJyUn8yicamsWl+Bpcvm8py/f1nGjiojf7xwep90V581p8bxu+9NYv0hHf/5xr/zSZ0kRieijdL6LOzO93maY19/qG+ZY3+Wd+fdw6kLNWyFXW/Wu6zxDhY2u42K5gqKE/2TX+/P5ZMu54VzXuDKyVcG5PpDMTllMknRSR6lY5zNSf1TMTUGc580jJOcpFjyU+JCJs/+zIYK3txRw+ohhL33YGtP0ZldD7GubGpDrRJkJcW4fe/NpxbTYOrgzR0Dc9ffVhp48vPDXDg7h5UzswddwxXzC1hYlMqDH+zvSY/5m3xN/rCEPS4ijtSY1KFPxpFfz9BEMynT9dD0yVkaxmijw6qePSyFvbqlmrPfOJvn9nruhBhojrUeo8PW4ZdSR1eohIqTx5yMWqUOyPWHQq1SszB7IZtqNw35geq0Exi4edruNpJcWJTK1iON2IKcZ99f18JfPjlElFrFnprmQd0BfbHv1be79ompbHR8byLV7n9lF41LZVZeEv9aV96nfK+1w8qv/7OLrMRY7j9/6pBrUKkEf/7BDOxScsebewKSksnT5PlsklZtcsw59WQfyWaXbDisZ2mvMsf+nCh71IVN2WNYCvuqklV02jt5ufTlYZVU+ZMyo8OJL1DCHgoszl5Mo6WRQ4ZDg543mAGYq4gdHOkYk8VK6THfTMf8QYfVxq//swttbCR3f38yHVY7BwfJ+8dGxJIRl+HVBqre4nqIdXVTu9v8uhMhBDefWkx1k5n39hzrOf77d/dRY2jn/y6bhTYmcpArnCAvJY47z53MhsN6Xtvm/5RMnjaPurY6r+cLgOOD0tON0901RprNXW7TME6WT8ygxWJlZ3V4lD2GnbBXt1TzXvl7zM6YTZOliXfL3w32koAT5l/+qGEPVZye75tqNw16ntOyt/fmaYulC5PF6j5i78mz+2e4ty88/skhDtSb+PMPpnPaJEdedmfV4Lav4xLH8U39N5itQ6c0Om2dNHc0u/Vhd1UR05/TJ2UwKVPDP78sx26XrC2p441va/j58mLmFg7s9B2MK+bns7g4lT9+UDrkYHEpJaXHWnho7X6W/vlL/vHl4JbCeZo8bNJGXat3U4xsdhu1rbUeC/u6gzpUApYUDz4YfHFxGmqV4MsD4VH2GHbC/nTJ06iFmseWPcb0tOm8uO9FbHbXm0kjSbmxnDFxY0iISgj2UgJGelw6E5MnsunY4MJusBiIUkX1mXzjriLGyRhtDEVp8UHLs2+paGTVhgp+ND+f0yaNIScplrSE6CEjvJtm3ERdWx2r9qwa8h7uJic1m7swtnd5JOwqleBny8dxuKGVl7dU8ru3SpiZm8ivzhg/5Hv7I4TgkYtnAHC7m5RMdVM7//iyjLP+up5zn9zAsxuPYGjr5JN99YNeu2dj2cuSx+Ptx+myd3ku7Id0zMxLIjl+cIfTxNhITs4Pn7LHsBL2apMjWv/BhB+QEZfBtVOvpcpUxRfV3rsP+ptyY7nfGpNCmUU5i9jZsHPQ0XlNFkcNe++cp1PY3aViABaMS+Wbo4YRz4OaLF3cuno3+Slx3HXuZMAherPzk9g1RMfi3My5nD/ufF7Y+wKHDYcHPdednUBPRcwQqRgnK6ZnUZAax33v7qPTauevl84aNDc/GLnJcdy5YjKbyhr591aHCDe2dvDS10e5+KnNnPLnL3n044Mkx0Xx4AXT+OauM7hsXh4H6k2D/n/yteTReb4npY6Gtk521xiHTMM4WRZGZY9hJezPlDyDSqi4ftr1gGPqUJ4mj+dKnguqiZRd2jnSfCQgpY6hxpLsJVjtVrbVb3N7jrFjoAFYT3OSm1QMODZQWzuslIzwSLj7uz1UHr9kFvG9Glxm5SVRoW8bspX/1jm3khCVwB++/sOgG8vuhlg7a9jz3ZQ69idCreLmUx1BxL3nTaEofXhPiT+al8+S4jT+tHY/1z6/jXl/+px7/7uPtg4rt58ziY23n8rqny7kygUFJMdHMSVbS4fVPqiPflpsGrERsV5voDojfE8i9g1leqR0X+bYH6fbYziUPYaNsNe21vJu2btcPOFixsQ7Zn2oVWqunXotexv3sv349qCuzWKzfCci9tkZs4mNiB3UXsBgMQyoYa8xtBMdoSItwf0j84Kika9n/2ivI0d986nFnFzQd7N3dr7ja9hVM3jUnhyTzG/m/IZdul28efhNt+fp27tTMf2qYiqbHALpyk7AHT88OZd1v13O5fO8mzDkCiEEj/xgBpFqFYePt/KTpUV8/D9L+eh/lvKz5eMGpM+mZjtsCvYdc/8BLITwyQysuqWaKFVUz+/4YKw7qCMpLpIZuZ5ZV0/J0pKhCY+yx7AR9qf3PI0Qoidad7Jy3EpSYlJ4Yd8LwVkYjsYkCO+NUyeR6kjmZc4btJ7d0GFwXRGTFDtoCVu6JprxGQkjlmdvMFn43VslTM9J5JenD8xRz8hNQoihN1DB8XM4N3Muf/32rz2ReX90Zh0qoRrwNFPV2E5qfNSAdvjBEEJQkOq/DuecpFi23nk6G247ldvOmcRENzXhAEVp8URHqIasYJqaOpVv6r+hucPzJ7BqUzU5mhxUYnDpstsl6w7pOGV8+oAOW3eEU9ljWAj7sdZj/Lfsv1w8/mIy4zP7vBYTEcPlky5nfc36IXOcgeK7UOrYm8U5i6k2Vbt9zDZYXAi7m+ak/iwcl8r2owY6rYH9xZNScvsbe2jvtPHXS2e6zFEnREcwIUPDLg9K5JwDUixWC49+86jLc/RmPSkxKQN6ESob272K1gNFTKQalQciGaFWMSlTw74hhP2qKVfRbm3n1f2veryGKlOVR/n1/fUt6Fs7PE7DOAmXssewEPanSxzR+g3Tb3D5+mUTLyM2IjZoUXtFcwUZcRlootxHOeHE4uzFAC6rYzptnbR2tQ5oTqo1mnsGbAzGwqJUzF029gyR/hgur22r5suDOu743iSKM9z/f5udn8SuaqNHezhFiUXcOP1G1h5Zy+bagU80TjuB/lQ1tQ+w6w11pmQnUlrXMuj3ZULyBJbnLeeV/a945KcjpaTaVO1Rfn39IcdT0dLxg5c59mfJeEfZ42h3exz1wl7XWsc7Ze9w0fiLBkTrTpJikrho/EWsrVhLfdvgZViBoMxYxrjE70a0Do6OyzxNnkvxctaw947YLV029K2dg26cOplfFPg5qEf1bTzwfilLitO4ZmHhoOfOykvC2N7F0UbP5o7eMP0GCrQFPLDlgQHNc67sBDqtduqazeT7Ma0yEkzJ1mJs7+JY8+AVJjdOv5GWzhbWHFwz5DUbLY2YrWaPhH3doQYmZ2nJ0Lq3YHBFYmwkJ+Unjfqyx1Ev7M+UPAM4fkAG46opVyGR/Hv/v0diWT04K2K+K2kYJ4uyF7G1fitdtr4VI04DsN7CXuNBqaOTlPgoJmVqAraBarXZ+fXqXUSqBY/+cMaQqYdZ3RuonuTZAaLV0dyz4B5qWmsG1La7shOoMbRjl3hUwx5KTM12jKzbN0QF08z0mczPnM+LpS/SYRt8oHZPqeMQPuytHVa2HzV4nYZxsnxiBvuOtfg0bjBUGNXCXtdax1tlb3FRsfto3UlOQg5nFZ7FmkNraOkcubb0urY6zFbzd07YF2cvxmw1s7NhZ5/jPXYCvVIxJ0odPROvheNS+bbSQId1+I1ndrvEZOmirtnM4eMmHvvkEDurjDx44XSyEof+oBmfoSE+Su1Rnt3J/Kz5rBy3kuf3PU+ZwbH/Ypd2Gi2NAyL2yiHsekOVSZkahIDSuqF/126acRN6s57/lv130POcezZDReyby/RY7dJnYXe+bzRXx3i0zS6EGAN8JKWcLYR4FpgMrJVSPtj9+oBjI8Gze58Fho7WnVw39To+PPIhaw6ucZuP9zf+npo0WpiXNY8IVQQbj21kXta8nuOuUjEnuk6HFlJw5Nmf33SUnVXGnhLIwahsbOOJzw/T0NKBqcNKq6WL1g4rrRYrbZ0DPxzOm5k9pAOiE7VKMCM3ySthB0dt+7qadTyw5QGeP+d5DBYDNmkbIOzVTmEfZRF7XFQERWnxQ26gAszLnMeMtBk8t/c5Lhx/IZEq13421aZq1EJNdvzg/2/WHdIRH6UeUJ7qKVOztaRrovnqkI4fzvF8mEco4WnE/hgQK4S4CFBLKRcB2UKI8a6OBWqxvalvq+etw29xYfGFZCVkefSeyamTWZi1kFf2v0KnrTPAK3TgFPbvQnNSb+Ij45mdMXtAnt2VAVitsZ0IlWCMh/nQ+WNTEWLoPLuUktXbqzn3iQ18su845i4bSbGRTMrUsnxCBpfNy+dXp4/n7hWTefii6fz9R7N5+YZ5PH7JTK++1tn5SZQea3Hrg+6KlJgUbj35VnY07ODtw2+7tROobGwnJlJFuibaqzWFAlOyEz0ybRNCcNOMm6htreWjIx+5Pa/KVEVmfCaRavdGZlI6yhwXFacRFeFbQqKn7PHQ6C17HDJiF0KcBrQB9cByYHX3S18AS4DZLo4FvK7w2ZJnkUiPo3Un1027jh9/+mM+qPiAC8dfGKDVnaDMWEZGbAbaKG3A7xVqLMpexBM7nkDXrusRLGOHEYEgMSqx57xag5nMxBiP640T4yKZmq3l64pGfu3mHENbJ3e+XcKHe+tZUJTC45fMItuDzVlfmJWXhNUu2XesmZMLPDfauqD4Av5b/l8e//Zxbp93O+CiOanRYf41kqMO/cXUbC3v7T6Gsb2TpLjBvVqW5i5lfPJ4nil5hhVFK1zWqdeYaoYsdazQt1FjMPPTZcNLfZ46MYM3vq1hV7WROV6ap4UCg36kCSGigHuBO7oPxQO13X9vAca4OebqWj8WQmwXQmzX6YaXu6pvq+fNw29yQfEFZCd49sjsZEHWAialTOL5fc+PyCCOQE1NGg0syVkC0KdZyWAxkBid2KdWu8Zg9qgipjcLi1LZVWV0GSVvPKznnCfW89n+4/zue5N49cYFARN16L2B6l06RgjBvQvupd3azmPfPAZAamzf1FJVU5vHVgKhxpQsRzDjSZ5dJVTcOO1GKpor+KLKtbdTlWlou15nXtzX/LqTE2WPozPPPtSzyh3AP6SUzp/YVsD5G5LQ/X5XxwYgpVwlpZwjpZyTnj68b/pzex3eL95G6+D4Zbpu6nUcaT7Cuup1w1rHUNilnfLm8u/cxqmTCckTSI1J7VPP7jQA681gPuzuWDgulU6bnW8rT1SjWLpsPPB+KVc+uxVNTCRv/3wxP1k2zqOmmuGQoYkhJynWp6aWoqQirp92PYYOx9fRO2KXUjpq2EfZxqkTZ2WMpx76ZxeeTb4mn6dLnh5Q/97c0UxzR/OQFTHrDukoSo8nb5h7Ej1lj4dGZz37UMJ+BnCzEOIrYBZwHo5UC8BM4CjwrYtjAeN423HePPQm5xefT05Cjk/XOKvwLLLjs3l+3/N+Xl1f6tvqv5MVMU5UQsXinMV8fezrHutkY4exT0VMl83O8RaLW7ted8wtTEGtEj159oP1Ji74xyae3XiEqxcW8N4tS5iWkzjEVfzHLA+cHt1x0/SbyNfko4nSEBNxYp9BZ+rA0mUftcKemhBNpjbGow1UcHg7XT/tekobSwdYUtSYHOP+BptzaumysaWicdjRupPlEzPYW9sypBd9KDJojl1KudT5925xXwlsEEJkA98DFgDSxbGA8dze57BLu0/RupMIVQRXT72ah7c9zK6GXczKmOXHFZ6gx0rgO9Sc1J9F2Yt4t/xd9jftZ1raNAwWQ88sUID6Zgt2Cblepko0MZFMy0lkc7me5zZG8fBHB9DGRPL8tXM5tXsIxkgyOy+JD/bU0WCykKHxrikmJiKGv532twHe5M5Sx+FGn8FkSrbWq6lXK8et5KndT/F0ydMszlncc9z5vRksx771SBMdVrvfhP3UiRk8+vFBljzyJSnxUeSnxFGQGkdBShx5KXEUpMZTkBpHhiY65PZAPHYVklIuBxBCLAfOBP4spWx2dywQNLQ38MahN1hZvNLjCeXuuLD4Qp7a/RTP732eJ057wk8r7IvT/Ou7GrEDLMxeiECwqXYT09Km0WRp6vNB6k1z0oBrF6Xyr3Xl7KgycsbkDB6+eAZpCcGpHulxeqwyctbUwXsqXFGUVDRgL6aycXSWOvZmaraWdYd0WLpsxEQOPY83Uh3JtVOv5ZFvHmHH8R2cNOYk4ERz0mC/99uONBKhEswf69mQ66GYkq3lxevnsb+uhcrGdqqa2thRZeC93cfoPXo3JlJFQUo8Vy4s4Mr5ns1iDTSe28V1I6U0cKIKxu2xQNDc0cy0tGnDitadxEXGcdnEy1i1ZxUVzRUBcV4sby4nLTaNxOiRSwmEGikxKUxJncKmY5u4acZNNHc0u2lO8l7YV0zP4r3dx/j5qeP40bzg/kJNzU4kQiXYWe2bsLuiqrENlXA/VWo0MCVLi80uOXTc5LF97kXjL2LVnlU8XfI0T415CnA0J2XEZvSZutWf3dXNTMrSEBvlv4HuyyakD3gC6LLZqTWYqWxqp6qxjaqmdnZUGbnnnb1sO9LEQxdN98qJMxCMqs7T8cnjefF7L3o8FmsoLp90OVHqKF7b/5pfrtefcuN3d+O0N4uyF7FHt4djrcewSVs/OwFHVJqV5F36AmB6biKb7jiNK+YXBD1KiolUMyVb63Oe3RVVTe1kJcb6XI8dCpzwZvc8HRMXGcdVU65iY+1G9jfuBxwRe57W/e+93S7ZXWNkpocfHsMhUq2iMC2eZRPSuWphIXetmMKanyzktnMm8sGeY6z820YO1Adv6DqMMmH3N6mxqSzNXcpnVZ/5fS6qlNIh7N/h/LqTxTmLsUkbHx11NJ/07zodo40mOsJ/UVawmJWXxJ4aIza7f6Z1eTrAOpTJTY5FEx3hVZ4d4NJJl5IQmdDjBTWUq+ORxjZMFisz8wIv7K5QqQQ/X17MqzctwNRh5YJ/bOKNb2uCshb4jgs7wFkFZ6E369ml2+XX69a31dNubVcidmBG+gwSIhN4v/x9YKBPjC9pmFBkdn4SbZ02DjeY/HK9qsbRW+roRKUSTM7WDjpNyRXaKC2XT7qcTys/pbSxFJ1ZN+jGqfNJaVaQhN3JgqJU1v7yFE7KT+Y3a3Zz+xt7vOpI9hffeWFfmruUaHU0n1Z+6tfrljc7rAQUYYdIVSTzs+b3fE/62gmYyRnFOeTezMpzfF3eNiq5orXDSmNbZ0gM2BguU7K07K8zef0kc8XkK4hWR/PA1w8Ag5t/7a4xEh+lZtww57v6g3RNNC/fMJ9fnFbMf7ZXc8E/NnFkkPmvgeA7L+xxkXEsyVnCp0c/9WsnqtMjRknFOOhduuYc/Wa3S46FUcRemBpHUlykX/LsVT0VMaOz67Q3U7O1mLtsHG30TtxSY1O5eMLF7G3cCzBojn13tZHpuYke21IEGrVKcOtZE3nhurkcb7Fw3t828sGeuhG7/3de2AHOLDiTBnMDe3R7/HbNcmM5qTGpJMUE99HQWzqtdvYda2b19mr++EEp2440+eW6zqlKQM8g6wZTB1026VOpYygihGBWXhI7qz3zZh+Mqu4B1qM9FQOOskHwbgPVybVTryVC5agwcRexd1htlNa1BC2/PhjLJ2bwwS9PYcKYBG5+dQe/f3dfwMc6gg/ljuHIstxlRKoi+aTyE781K42GihiTpYv9dSZKjzWz71gL+461cLjBRJfN8cgsBLyypYrVP1nI9NzhlWxmJ2RTqC3kePvxnu7KWqMjKvXUrnc0MCsviXWHdJgsXWhi3LsQDoWzhn00Nyc5GZ+hIVItKD3W4rEdspPM+EwuHn8xG2s3ujXS21/n+JmdNQIVMb6QnRTL6z9eyCMfHeDZjUc4ZjSz6uo5Ab2nIuxAQlQCi7MX82nlp/x2zm+HXTonpaS8uZyV41b6aYX+w9Jl4/73Svm6XN9nnFtqfBRTsrUsnVDElGwtU7O1aKIjuPCfm7nhxW945+bFwzbSumj8Rew4vqPn387mJG+7TkOZ2fnJSAklNc0sKvZu3mZvKpvaSYqLJDHW9w+HUCEqQsWEMRqvN1Cd3DHvjkGnK+3u9uhxmrGFIlERKu75/hTmFiaTPITTpT9QhL2bMwvP5Kuar9ir38v09OnDutbx9uO0dbWFXH7darPzi9d28tn+45w5eQwXn5TbLeKJjNG6bot+/rq5XPzPzVz/wje88bNFw2q8uG7adVw37bqef/c0J4VTxN4dNe6sNg5L2KtH4QDrwZiSpeWLAxNJuRAAACAASURBVA1IKb0OnCJUET3pGFfsrjaSoXH40oQ650zzbHbEcFFy7N0sy11GhCrCL9UxPRunIZSKkVJy77v7+LT0OPd9fwqrrp7DL04fz+mTx5CZGOP2l23CGA3/vPIkDje0csurO/w6eKDWYCY5LpK4qPCJLxLjIilKix92ZUxlY/uoG2A9GFOztTS2ddJgGnyuqS/sqjYyMy8p6E1qoYQi7N0kRieyIGsBn1R+MsAy1FtCUdj/9kUZr26t4mfLx3Ht4rFevfeU8ek8cP40vjqo4w/vlw77++PEF7ve0cCs/CR2VRt8/j512ezUGs3kp4TP92ZKdweqt41KQ9Hc3kWFvi3o9euhRviESn7grIKzuHfzvexv2s+U1Ck+X6e8uZyUmJQBvuO92XBYx8MfHiA2Uk1CTAQJ0RFouv+bEB1JQkwEmugIEmIiGKON5qT8ZJ8jkte3VfH4p4e46KQcbjt7ok/X+NH8fI42trFqfQWFqfFcv8S7DwdX1BjMjEsPn6jUyey8JN7aUUuNwezT5ucxoxmbXYZFqaOTyVkaAPYda/ar++aeWseT0UhYCYwmFGHvxal5p6IWaj45+snwhN2DiphXt1ZR1djO9NxEmto6qWps7x60bMXsolPtjMkZ/Omi6V5bwn5aepw73y5h2YR0Hrl4xrAeV+84ZxKVjW088EEp+SlxnDHF5bAsj5BSUmsw+81iNZSYnd/dqFRt9EnYq7rtesOhOcmJJiaSgtQ4n0oeB8O5cTrcqq1wQxH2XiTFJDEvcx6fVn7Kr076lU8i6PSIWVG0wu05VpudjWV6zp2exSM/mOHy9bZOG63dQr/hsI5HPz7IWX9dz4MXTOP7MzwrGfu20sAvXtvBtJxE/nnFSUSqh5d5U6kE/3fpbC5d9TW/fH0nq3+y0OdhFob2LsxdtrBpTurNxEwN0REqdlUZvS7vg152vWEk7ODIs/tb2HdVN1OUHh8W1UP+RMmx9+OswrOoMlVxyHDIp/c3tDfQ2tVKcVKx23N2VRsxWawsdROtRqhVJMZGkpMUy8RMDTeeUsQHvzyFgtR4bnl1J7e8ugNDW+eg6yhraOWGF78hUxvDc9fOJd5PNqKxUWqeuXoOSbGR3PDiN9Q1m326Tu0wfNhDnUi1iuk5iezysVGpqqmdqAgVY7x8Ogt1pmRpqWxsp8XS5ZfrSSnZVW1U8usuUIS9H6fln4ZKqPik8hOf3u+JR8z6QzpUApZ4UQ5XnJHAmz9dyG/PnsjH++o586/r+az0uMtzj7dYuOa5bUSoBC9dP9/vwycytDE8d91c2jps3PDCdto6rF5fw9mcFI4ROzgMwfYea/Gpy7CysY285NiAz2odaZwWvgfq/GOSVtdsQd/aoQi7CxRh70dKTApzx8zlk6O+Vcd4UhGz7pCOWXlJJMZ59/gYoVZx86nF/PfmJaRrornxpe38Zs3uPhFQs7mLa57bhrG9kxeumxewPO2kTC1//9FsDh438cvXdnpt8NTTnBSGETs4DME6rXb213mfeqhqMlMQRqWOTk5YC/hnyNquamXj1B2KsLvgzIIzOdpytEekPUVKyda6rSRHJ/cYXfWnqa2TPbXNLJvge2XAlGwt/715Mb84rZi3d9Zyzl/Xs/Gwng6rjZ+8vJ2yhlb+ddXJAR/mvHxiBr9fOZXPDzTw2CcHvXpvjcFMQnRE2OZGnaPydlZ5l46RUlLV2DbqfdhdkaGJJi0hym8lj7urjUSpVUzqrrhROIFHwi6ESBFCnCmE8L2VbhRxesHpCITXzUpvl73Nupp1XD31arfnbCzTIyUsnTC8b2VUhIpbz5rImz9bRGyUmiuf3cqKJzeypaKJx344k1PGj0y1yVULCrhwdg7PbzpCs9nz3KnThz1cm0qyEmPI0ET3RJWe0tjWSVunLSyFXQjBlOxEv22g7qo2MjlbGxZDWvzNkMIuhMgCPgDmAV8KIdKFEM8KITYLIe7udd6AY6OVtNg0Thpzkld59jJDGQ9tfYgFWQu4ftr1bs9bd1BHUlykx/Mfh2JWXhIf/PIUblwylgpdK3edO5kLZuf45dqecsOSsVi67LzpxcSYWkN4Nic5OeH06J2wh2tFjJMpWVoON5iG7XBos0tKapuZpZQ5usSTiH0q8Gsp5R+Bj4HTALWUchGQLYQYL4S4qP+xwC15ZDiz4EzKjGVUNFcMea7FauG3639LXGQcD53yECrh+tsqpWT9YR1LitP86hsdE6nm7u9PYd/953DTUv8P5R6KaTmJzM5P4pUtlR7vS4TT5CR3zM5PprKxnaYhKph6E052va6Ymq2lyyaHPWWqrKGV9k5bSFr1hgJDCruU8jMp5RYhxFIcUfvZwOrul78AlgDLXRwb1ZyRfwYAnx4dOh3zyDePUGYs409L/kRarPsUy/46EzpTh9syx+Hiz+ns3nLVggIq9G1sLm8c8lyTpYtmc1dYR+xwYkybN2WPVY3OTeXwFPbheLP3psfRURF2l3iaYxfApUAXIIDa7pdagDFAvItj/a/xYyHEdiHEdp1ON9x1B5wx8WOYlT5ryDz7R0c/4o1Db3D9tOv7TAlyxfrDjq976Qjlv0eSc6dnkRwXyStbKoc81+nqGK4VMU5m5CaiEng1UamyqY1MbQwxkeGZNy5MjScuSj3sDdRdNUa0MREUhmH1kD/wSNilg5uBzcACwPkbmdB9jVYXx/pfY5WUco6Uck56+ugQtjMLzuSg4SCVLa7FqtpUzf2b72dG+gxumX3LkNdbf0jHpEwNmYnh1XgCjnTQJXPz+KT0OPXNlkHP7WlOCvNUTHx0BBPGaPjyoM7jctCqxvawshLoj1olmJSpGb6wVzkcHcOt1t9feLJ5ersQwlnmkQQ8zIlUy0zgKPCti2NBY29tM5f8v6/54oDrBh5PObPgTACXUXuXrYvb19+OQPDnpX8mUjV42V5bh5VvjjYFLA0TClwxrwC7lLy2rWrQ88LRh90dN51SREltM3/91LNO5sow82F3xdTsRErrWrB72fvgxNxp4+Bxk1K/PgieROyrgKuEEOsBNfBO978fBy7BUTHj6tiII6Xk31srueipzWw70sRv1+wZsvV+MLISspiRNsOlsD+580lK9CXcv/h+chKGrkLZUtFIl02GpemVk/zUOP5/e2ceF1W5//H3w46AC4i7oOCK+5ZomUtaapaapZa5pGnaetsz63Zv2628t37ZbouWW+aaaaZZ7mIqJrgvmSyuqIAbgsDz++MLijgDM8PALJz368ULODNz5jlz5nzP83yXz7dro1DmbEnkShG67UdTM/Dx8qBqgJmK2PMnYef8Uhpl2TKoXR2GtK/Lx6sPFTvRyMjKIeV8ptsGTvOJqlWRC5nZJKVeKv7JJth9LJ2cXG0ETovAkuBpqta6l9b6Vq31o1rrdCRYuhnorrVO11qfK7ytNAdtiouZ2Tw9dweTFu0iOiKEWQ93JD3jCm8s21Oi/fYK78WeM3tIPn8tlW998nqm757OkMZDrs7qi2PdgRT8vT1pX8+8lK87MDw6nFPnM/nVjNwBQHJeRozZZXTMx7BgDBxeUzqDLGP+3b8ZTWtW5Om5cSQXYczyVR3doc9pUTQrYQD1WsWpkepoDpsqT/OM/Q9a6xNFbSsrDp48T/9PNrIk7hjP9mrE9FEduLlBVR7pGsHC7UdZe8D2YG3PcMmOWZWwChCRr0kbJtGoSiOea/+cxftZeyCF6Ihgty+m6Na4GrUr+zMjxnwQNTk1o+jAacIm+f3b62Cnph6OxM/bk8+GtSU3V/PorO1kZt8oywyiEQO4pZxAQRpVD8LTQ9nsZ49LTqdWJT+q2doK79JZ2PB/kGGbSJsr4PKSAov+TObujzeSdimLmWM68sRtDa/OBJ/o0ZCI0ABeXrjTJqEqgDpBdYgKiWJlwkpycnOYuH4il3MuM/nWyfh5WfbFSjxziSNnLrm1GyYfTw/FsOgwYg6f4ZCZXOWjqUXksGddhOM7IDgCjsbCPod49exOvaoBTL6vFfHJ6by5dK/J5+TP2N3dx+7n7UmD0ECbNWPiktJsb1yddRFm3QerXoNpd8L5Mp+Hlgkua9gvX8lh4sKdPD03jha1K7HsyS43NA/28/bk3UEtOZqWweQV1mmZFKRXeC92nt7Jm3+8yZYTW5h400QiKlteCLQ2P82xHBh2gMHt6+Lj6cHMzTcGUS9fyeH0hUzzhv1oLORmw+1vQUhD+P0NyDU9w3U1ejevwbhbI5ixOYEfdxy94fHEs5cI8vOispXicK5Is1oV2WODQNrZi1kknr1kW+A0JxvmPQTHtsOtz0PqEfjmDjj7t/X7cnJc0rAnnLnIoM82MWdLIuO7RjJ7bEeqm1mWdagXzPDocL6NOUJsgm1Lr3w/+vwD87kz4k4GNBhg1evX7k+hThV/6ld17yV2PlUDfenTogYLYpO5lHX9SulYcRkxCTGAgvDO0GMSpOyD+B9MP9cFef6OxnSoV4WJC3dy8OT1K5qEM5cIC67gtvo5BYmqVZGT5zI5fcG65tZxyXn+dWsDp1rD0qfg4Aq483/Q4xUY+RNcThfjfmKXdftzclzOsP+y6wT9PtpAcmoGX49sz0t9muBVTGegF3o3pkZFP15aEG/Wv1kU4RXDiQqJIiwojFejX7XqwsvKziXmr9N0bRRaLi7YfIZHh3M+M5sfdxy7bvvVVEdzM/bETVC9OfhXhqb9oUZLWPM2ZNue3eRMeHt68PEDbang48mEWduvcxEmnr3k9hkx+eRXoFrrZ9+RmIaHghbWKpeufgv+nAldX4T2eVpOddrBQ7+A8oTpfSFxs3X7dGJcyrBvOHia8TNjqV81gKVP3MJtTS3ruRnk581bA5tz8NQFPlltnRRvPp/1/IzZd84mwNu6WXdsQioXs3LKjRsmn3bhVWhSI4gZMdfrx1zVYTflR87JhqStEN5J/vfwgNteg7RE2P5tWQy7TKhe0Y8pQ9twOOUCExfuRGtNTq4mOfUSYZY0sHZEQFlrOTcnd9vFNRZV07bMmLjkNBpWC7KuI9jWr2DdZGg7ArpNvP6xak1gzAqoUBW+GwAHrVN0dVZcyrB3jgzhjQHNmTe+k9UpYT2aVKd/61p8tuYQ+09YL0AU7BdMJV/r06vWHUzBy0PROTLE6te6MkophncKZ8/xc2wvUFJ/NDUDTw9F9SATOewn4uDKRQiLvratwW0Q1hnWvieBLzehc4OqPHt7Y5bEHWPm5gSOp2dwJUcXP2OPnwfvRcjvsiJ5G0y/E77uCZ91hnfCYHo/WPUvCW6ft74QsHIFH2pX9meXFQFUrTVxSWm0qmvFdbhnCSx7Dhr1gTs/AFOr5sphMHoFVG0Ic4a6RQ2FSxl2Dw/F8Ohwm1MG/9kvikBfL15cEG91xx9bWXcghbbhVQjyc/+AWGEGtK5NoK/XdfoxR9MyqFHRz7T7LH8pHNb52jaloOdrcPEU/PFFKY+4bJnQNZLujUN5fekefoo7DlC8DnvsNMg4CwsfhsWPQuaF0hvgmb/ghxHw1W1w+gD0mQwDp0LrByDrAmz6CL5/AP7XCD5oIYHJmE8gaYtFs/pOkSGs2HWCTYdOWzScpLMZpF66Yrl/PWETLHgY6nSAe78BzyJm+YGhMGop1O0or9nypWXv4aS4lGEvKSGBvvzr7mbsSEpj+qYjpf5+Kecz2X3sXLlIczRFgK8Xg9rWZln8cc7kBcmK1GFP2ARV6kHFmtdvD4uGhnfARvfKPfbwUHwwpDXVgvx495d9QDGG/dxx+YxufUF+dsyGqd3geLx9B3bhFCx7Fj65CQ6ugq4vwZN/Qsdx0GoI9J0M49bAxGQYvVIymOq0g+StsOJl+LqX3HSK4Z93RVG/agDjZ8Zy6FTxN6gdyVYoOp7cI7PvymHwwFzwsWCF71cJHlwAjfvAz8/Bmnddto6iXBl2gLtb1aJ741D+u2I/SWdtK2m2lPV5aY7l1bADDIsOJysnlx+2SeXu0bQM6pgKnGotM/aCs/WC9HhFMhg2fVSKoy17Klfw4dNhbfHx9MDbU1GrKGG0vUsADS3ulYyhkUsg8zx81RP+mFpyI5R5Ada8A1PawLZp0HakGPTuE8HXRPs5b38I6widH4f7psPTu+DZ/dBhLMR/D8fjiny7in7efDOqAz5eHjw0fcvVm7854pLS8PP2oFH1YlrhpSfDzEHg5Q/DF0IF020qTeLtD4NnQKv7JWi//EXILVlTEEdQ7gy7Uoo3B7bAQ3E1cFVarDuQQkiAz9VAUXmkUfUgOtYPZvaWBDKzczhx7rLpGfvpg3Dp9LXAaWFqtoTmg2DzZzb5dO1KTrbcYI5stMvuWtWtzH8Ht2LMLRFFN2DZvRiqRUFoY/m//q0wYSNEdIXlz8P3w6Sq0lpyrkiAcUobWPMfiOwBj22Bfu9DkGUJClcJqiE3Yb/KsPrtYp9eN7gCX45oz6lzmYybEcvlK+ZdOHFJaTSvVQnvorLgLp0Vo551QWbflcOsGz+Iy6b/pxD9KGz5Aj6/GfYudanZe7kz7CCpdi/1acKGQ6eZb0U7N2vIzdWsO3iaLg2rlntp0eGdwkk6m8EPW5PIydWm5QQSY+R3mBnDDtB9EmRnwvr/ls5ALeHSWZh1L6x8BZY8brfZ3N2tavFSnybmn3DumHxGUYVqKAKqwv1z4Y634eBK+LxLXi1AEeTmQsoB+HMWLH0aPm4vrpeQSBjzKwyZAVUb2H4w/pXh5ifhwC+SSVMMbcKq8MGQ1sQmpPL8/HiTqo9XcnLZeTS9aP/6lQzx+Z89DENnQY3mth+Dh4d8pvd+I9+5ucPgy+5waJVLGPhyadgBhnUMp0O9KryxdA+nzhetH24Lu4+d4+zFLLo2Lr9umHxuj6pBaJAvH/52CIDalU34OxNjICAUQoowKCGR0OZBcROkFt/QA5CLfe1kmNxQsjiulOBcn9oLX/aAIxugxWAxIAd+sX1/1rAnzw3TzERxnIcHdHoMxqwET2/JyV773rUA5qWzcGClzKBnDIR368EnHeDHRyUDpEp9GDoHHloOdW+yz3g7jpfz+fvrFj29b4uavNC7MT/FHeODVTdKHO8/cZ7M7NyiDfumj+V7NPALWc2UFKVklfjYFuj/CVw8I6uBaX3kO+DElFvD7uGh+M89Lbmcncu/l5RMAdIU+d2SurhhtyRr8fHy4P4Oda9WGZp0xSRskiBpcUVcXV8E5SG+4KLQGnYtgI87wOo3oWIt2PCBLKuLm9GaYt/P4svOugijlsGAz6BSXdj8qfX7soU9i6Fas2tuGFPUbguPrBNjtPot+KIrTGkL79WH2fdJLveFFGg+UAzVY1vgxQQYsRia9C3+s7cGnwDo8iz8vQ4Or7XoJRO6RjKkfV0++v3QDSvp/IrT1uakBHKyJWMoojs0v6dEQ78BTy+ZUDwRK1WrqUck/fO7/hatSBxBuTXsAA2qBfJ49wYs23mc1ftP2XXfa/en0KxWRaoGmtEcL2fc3zHsqv+4ZuEOUueOQVqC+cBpQSrVhpvygnOn9pl+ztFYKROfP1rcAqOWwSNrYfhiyMmSGdfPz1uWKqi1GMTvH5DVxLg1EjD09IKbxsGR9cUGCUtMvhvG1Gy9MH4V4Z4vxUesc6BaUynyGrkUXkqCCRvgrg/FUIU2ltl+adHuIahYG35/0yL3hcS/mtM5MoSJC+OJKdA/Ny4pjeAAH+oGmwkuH/oVzh2F9g/Za/Q34uUDHR6WgPLtb8GJnZLbP3uI/TOTSki5NuwAj3SNIDI0gFcX7yIjyz5iU+cvX2F7Ymq5zoYpTM1K/tweVZ2w4Ao39vPMl+ktWJhUFLc8A94VZCZekHPHYNF4cZec/Rvu/gjGrYV6ec29IrvDhBjo+IjkKX/aCf763fz7ZF2EeaPEMLW4F0b/IjeWfNqOAJ9AiCnlWfueJfK7sH/dHEpBm2HwaIz4mrs8A/W7gG9g6Y3RFN5+IraVvEX8/5a8xNODz4a1Iyy4AuNnxvJXitx845LSaVWnknlZjm3TILA6NO5rr9EXMUh/yQR6Kg56vCo33S+6wKp/O43/vdwbdl8vT94a2ILk1Aw+/O2gXfa56a8zZOfqcicjUBzv3duSOeNMGO/EzWIga7S0bEcBIdDpcdj7k8zOsy6JT/mjduJ+ueVpWTa3HQEehW4ivoHQ510x0l6+4nNe/NiN+fFpiTLr3/Mj9HpdZsHehWaL/pVl5rtrvuSYlxa7F4l+Tmij0nuP0qLNg1Kb8PubFgeaK1XwZtqom/DyUIyevpWks5c4cOq8ef96WqLcONqOkBhDWeEbBLc+B0/FQ5vhsOF9WQk6QXpkuTfsANERIdzXrg5frT/MvhMla7IL0lQj0NeLtmHu3S3JWoL8vE2LfyXGSHVgUZWBhen0GPgHw5In8/zob0HDXvD4Vuj5L3FJFEVYNIzfILP/uDnwSbSktIGkMU7tDqmJMGwe3PyUef9zx/ESpNxaSpWK6UchabPls3Vnw9Nb9FlOxOfl4VtGWEgFpo5oz/H0ywz+Igati1B03P6d/G47wvTjpY1/ZVkddn5Cvgc/PiY+fwdiGPY8Xu7blIr+3ry8cKfNTXZB9CzWHUihU2QIPl7Gx1ssGWkiLBVugX+9IH4VZbZ0cpfM4Ef9DIO/k9mhpXj7iVzB2N8lg2PuMPj2LvjubrlYx/4mN4uiCK4PTe6Ebd/IysHe5BtDS/zrzkqL+yC0iWTlWCEg1i68Cu8PbsXxdMlkMqnBnnNFDHvD223LWbcXSkGvN6DbyxA3GxaMdqgiqWF58qgS4MPLfZuyPTGNOVtvbBBhKYdPXyQ5NcNww1hK0h+ALjp/3RwdJ8DDv8HYNVDvZtvHUKs1jFsthTWJmyWz4uHfRBTKEjo9Lq6cuDm2j8EcuxdB9RaWj8UZ8fCE7i/D6f1Wa+v3a1mL1+6Kon/rWgQH+Nz4hP3L4cLJ0g2aWopS0O1FCazu+VEmClcyHDKUYg27UqqSUmq5UupXpdQipZSPUuprpdQmpdQrBZ53wzZXY1Db2kRHBPPu8n0257av3Z8nI2CkOVpGYgx4eEPtdta/1sMD6rS3T2aHp7cE+p7/S9wv/lY0cgiLhlptJPXRnv7V9GS58TXrb799Ooomd+Vp6/9HZtlW8NDN9flwaBvTD277BirWkRm7s9D5cej3fyIBPOs+kX0oYyy5IoYB72utewEngKGAp9a6M1BLKdVQKXVP4W2lN+TSQynFWwNbcPlKrtm+lEWxYvcJJq/YT1TNioQVJ7+672fx42762LYycHchIUZmzJaINJUFfhWtz+dWSmbtZw5ZnP1hEVezYQbab5+OwsNDMkjSEuDPGfbZ59nDcHi16SC5o2n/ENwzVTK+vhtQ5uJ1xRp2rfWnWut89flQ4EEgfz31O3AL0M3EtutQSo1TSm1TSm1LSUkp6bhLjcjQQCZ0i2RJ3DHWHbBsnFprPl/7F+NnxtKoRhDTR3co7gWw9h3xLa+cBP9rIml6SVtKP10qNxdiv5WiEUdH769clv6TtrhhnI2o/pKzvfkT++1z9yKo0aJk5f3ORMNeIou7dnLJKoDziZ0u3Y/aDi/5vkqDloNh8LcSOJ5+lxSHlREWr2GVUp2AKkASkN+J9xxQHQgwse06tNZTtdbttdbtQ0Od200xoVskEVUDeGXxriJFiUBa370wP553lu+jb4uazB0XTbUg0/1Xr3J0uxS19H4bxm+UlLC9P4nc6eddYOvXpbd8++1f8NOTEiCc0koqOC0tz7c3R2OlYMjawKkz4uktBUt/r7NPsUp6suR/u2o2jCmUkjjG+WPiQikJ2ZmiddO4j1QVOytN74L758hqbnpfyXIqAywy7EqpYOAjYDRwAcjPWQvM24epbS6Ln7cnbw5sTuLZS3z0u/nc9rMXs3jw6z+YF5vMk7c15KOhbW4svjHFtm8kb7vlEBEq6vc+PLtP/HIKWPaMzOJ/+od9K9o2fwYbP5SKwEFfQ3CEGPYPW0o2SPwPZRvsScwrTKrbsezeszRpNxK8A+RzLil7fpTfzdzADVOQ+rdC/a6w/n8laxKy9ydRA3WGoGlxNOgpSpPnjsO03lI8V8pYEjz1QdwsE7XWCUAs11wtrYAjZra5NJ0jqzKobR2mrjvMgZM3zp4PnbrAwE83siMpjQ+HtuaZXo0sU3HMSJUimhb3Xa9x7RskX9JH1ktGRlR/ybL4ogt8fbt0sykJuxbALxOhST/Ru2hxL4z4Ef4RLylaqQmwcCz8t5HcUJK3lb5bKHEzhDa1Ti/bmfGvIhWfO+fB+RMl29fuRRJsDIm0z9iciR6vilH+43Pb9xE7HSqHQ0QPuw2rVKl3M4z8UVbiy54t9bezZGY9BmgHTFJKrUHmlMOVUu8Dg4FlwGIT21yeSXc2JcDXi0mLrs9t33DwNAM/3cjFzGzmjI2mf+vaReylEDvmQHYGdBhj+nGlJNNjwKfwzF644z+iVf5lD/h7vW0Hcnit+PDDomHQV9cHmiqHSYrWkztg5E9Skh33vbRD+zRaKjqPx9vfyOfmSEzBnP66q9JxPORmi765raQlSTciV85dL4q6HaBRb9g0ReoYrCXlgGj0tBtVulo39qZ2O6m3GGCHFV0xWBI8/UxrXUVr3S3v51skWLoZ6K61Ttdanyu8rTQHXVYE5+W2bz2SyrzYJABmbk5g5LQt1Krkz+LHbqZduBXVpVqLG6ZOBwmKFUeFYOj0qBTKBFaDGQOuVdlZyvF4acAQHCm+vsJl8fl4eMgy+Z4v4Ln9IhTlV0mKSr7oAh80E+3uAyvs4645uQsyz1km/OVKhETKzXHr17Z/TvluGHfyrxem+yTpiLXhA+tfGzsdPPIUF12N6lHWNy+xAStquK+htU7lWhaM2W3uwH3t6jA/Npm3f95HfHI6s/5IpEeTaky5vw2BvlZ+fEfWw5mDMMDKJWhwhDRAmDcKljwhjycv8QAADEZJREFUjYV7/rv4FK/UBGkK4VcRHpwvrgJL8Ksks6F2o6T/5YEVojseN1duTF7+ENENGveWXqSFe5RaQr50rrvN2EHkDvYvk5WPLT7g3YugZiv3dMPkU7OltJ/b+H/yvbzlH5a97koG7JglQcnAaqU7RhfGhdYxjkEpxdsDW3ApK5tZfyQy5pb6fDmivfVGHWQW51/FtiW2f2UYNl9kQzd9BHMfLDr4dPEMzLwHsi9L4KZSHevfE+TiaTtcVAJfOCz7avOgzLh/egrebyK63/HzrNtvYozomds6LmcmvLMYZlsKltIS4eg2956t53PXFNGOX/UarHzVMnff7sVwOU0SAAzMYtOM3em5nC7iUPVugfajS1y80KBaIFOGtuFKrubuVjamVp0/AfuWig/WnDukODy9JPBZtTH88iJ80xse+P5G45h1EWYPlpS54YtFk9seePtJhL9BT+lUf2ovHFgOuxZK4NWnguimFIfWYtjrd7XPuJyN/IKlhWPhLwv0ZgpyNRumHBh2Lx9RzfSrnOdvT5XMsKLE4GKniS6+PTokuTHuN2PPzYWF46TjzM/PSUbJiZ0l3m2fFjVtN+og1Xa52XKjKSkdx8ED86STy5c9IDn22mM52TDvISn8GfRV6bk6lBJ/YZdnxU1Uqw0seBiO/Vn8a88eFn0Pd3TD5BM1AIJqQszH1r0u3w0THFE643I2PDxlsnLrC3KNzB9lvnjp5G6RWGg3yr7dntwQ9zPsa98Rf3Df/8psIPWIuApWviozWUeQmyPVnhHd7Oc3bdgTHv5VNMWn95VZs9aw9Ck4uEKOv+ld9nmv4vCpAPd/DxVCYPZQWSkUxdXG1W4WOC2Il48ULB1eIwbJElITpGjL3XLXi0Mp6DEJer8j+emzzeirbJsGnr7Q6oGyH6OL4V6Gfe9SWPsutH5QfNEtB4s+d5thstT7JFqa+pY1B1dCepJ9ZusFqdYUxq6Gmq1h/kNSZPTnTJn9mEunLC2CqsMDP+S5gYYUXTmbECOxhqou2DjCGtqNkk5PPz0lTaOL0wspD9kwRRE9QRpRH9ko3+WL11rjkXUR4ueKiyogxHFjdBHcx7Cn7IdFj0CttrK0y1+qVQgWEfyHlotve/Z98MPI0u14U5ht30BgjdJp2xVQFUYugZZDJeumzXCRSHUE1aNg8HTxvc8fbb7ZQGKM6MO4Ug6yLVQIhtvfENfTgjHwXiRMu1OC36dNVDTvXiQ36eD6ZT9WZ6HVUAnUn9orVZr5q79dCyQ91giaWoR7XFmX06XZsLc/DJkpQb7ChHeWjjk9XhEN509ukr6XVgj/20TqEZHvbDey9Np2efnCwM+l0fJdHzrW/5gfWD24ElZMvPHx8yfh7F/uIfxlCR0ehucOShziln/IrH3lK/Bxe5jSFn55WfRlzvwlcZHy5oYxReM+8OBCSTj4+g65CW77RqqULe2LW85x/ayY/GBp6hGpnKxURBWol49obje7R/RYfn5Oco0Hfl56jQxip4uhLe22XUpJANMZ6DBGZqkxH0thVPT4a4/l+9fdQfjLUjw8oe5N8nPbPyWlMb82YOuXogip8jK3ykM2jCXUuxlGLYWZg+DL2yAzHfq8ZwRNLcT1DXvBYKmlxiIkUtIAd86D5S/CnKGi0WJvTfDsTNg+Axr1cc987aLo9brcbFdMlHZ1jXvL9sQYKXCq2cqRo3MslcPgprHyk3lBAqwHlkvanzWt/dydmq1g9ArRM8/NFtE8A4twbVdM4WCpNSglwdX7pomk5qrXSmF8eQp0HewcNHUFPDyl0UCNluJvPx4n2xNjRAunLLvJOzO+gdC0H/T/BO54y9GjcT5CIuGRtfJjTVerco7rGnZzwVJriegG0Y/ClqlwaJU9Ryh+wSr1XEeBzt74BMADcyUDZvYQOWcndpYvN4xByakQ7No9Xx2Aaxp2S4Kl1nDbP6WL+uLH7Nem7tReSNgoUXx3z/4oiqAaYtwzL0ilrM4tP4FTAwMH4XoWp2CwdPB3RQdLLcXbX9wGl85IUNUeErXbpoGnj2sq0NmbGs3F5XU5TYKEdYppHWhgYFAiXM+w5wdLe79j3yV9zVbQ7SXJJd45v2T7yrooTTKi+kueuYHopQz8QiQIfAMdPRoDA7fGtbJi/l5ne7DUEm7+h+RfL3tWdExszWTZOV+KKdqXcfWns9NysKNHYGBQLnCtGXtYJ+j1RsmCpUXh6SU57bnZsHiC9ZKr+Wz7BqpFGcUUBgYGDsG1DLunN9z8ZMmDpUURHAG9/yOrgy1fWP/6o7FwfIfowhjFFAYGBg7AtQx7WdF2hBQV/foanNpn2Wtyc8U/v2CsdKo3iikMDAwchEWGXSlVXSm1Pu9vb6XUUqXUJqXUaHPbXBql4O4p4BskzRKys8w/V2vY/wtMvVVa13l6w5AZ0o7OwMDAwAEUa9iVUlWAb4GAvE1PANu01p2BfkqpIDPbXJvAaiKodSJeAraF0Rr+Wg1f9YQ5QyRP+54vYcImaHBb2Y/XwMDAIA9LZuw5wBDgXN7/3bjWtHoT0N7MNtenaT/JQ9/wPiT+cW17QgxM7wczBogC3V1TRPe95eASt+EzMDAwKCnFpjtqrc+BNHXOIwA4mvf3OaC6mW3XoZQaB4wDCAsLK8mYy5be78Df62HRONHz2PCBSA8EVoc+k0WO18vX0aM0MDAwuIotwdMLQH435sC8fZjadh1a66la6/Za6/ahoaG2jNUx+AZJCmRqAky/E45uF+XCJ3dI71HDqBsYGDgZthQoxQK3APOBVsBmM9vch/DOEky9mAIdxhqBUQMDA6fGFsP+LfCzUqoLEAX8gbhhCm9zL0q7UYaBgYGBnbDYFaO17pb3OwHoBWwEemqtc0xtK4WxGhgYGBhYgE1aMVrrY1zLgjG7zcDAwMCg7DEqTw0MDAzcDMOwGxgYGLgZhmE3MDAwcDMMw25gYGDgZhiG3cDAwMDNMAy7gYGBgZuhtD0aN1v7pkqlAAmFNlcFTpf5YEoH41icE3c5Fnc5DjCOxVrCtdbFarI4xLCbQim1TWvtFqqQxrE4J+5yLO5yHGAcS2lhuGIMDAwM3AzDsBsYGBi4Gc5k2Kc6egB2xDgW58RdjsVdjgOMYykVnMbHbmBgYGBgH5xpxm5gYGBgYAcMw25HlFJeSqlEpdSavJ8Wjh5TeUYpVV0ptT7v79pKqeQC58aF2ni5B0qpSkqp5UqpX5VSi5RSPq58vSilgpVSvZRSVR09lsI4hWFXSn2tlNqklHrF0WMpIS2BOVrrbnk/Ox09IFsoZBC9lVJL887PaEePzVKUUlWQpjABeZs6Am8VODcpjhuddZgxiK54zQwD3tda9wJOAC/hoteLUqomsAy4CVitlAp1pnPicMOulLoH8NRadwZqKaUaOnpMJSAaGKiU2qCUmqWUsknv3pGYMIhPANvyzk8/pVSQwwZnHTnAEKS5Osi5eVQpFaOU+sBxw7KJwgZxKC54zWitP9Va/5r3byiQjeteL82Ap7XWbwErgB440TlxuGEHunGtQcfvSO9UV2Ur0FVrfQuQBvR18HhsobBB7Ma187MJcIoCjOLQWp/TWqcX2LQc6Ky17gQ0Ukq1dNDQrMaEQXwQF75mlFKdgCrAr7jo9aK1XqW13qyUuhWZtd+BE50TZ7hDBiA9U0GMSQMHjqWkxGutM/P+3ge4xEyqIFrrcwBKqfxNhc9PdQcMyx5sMnFu4h04HqspYBCP4KLXjFIqGPgIGASccOXrRclFMgS4Aiic6Jw4w4z9AuCf93cgzjEmW5mhlGqllPIEBgJxjh6QHXCX87NCKVVTKVUBmV3tcvSArKGAQRyNi54TpZQPMqudmNcn2aWvFy08hqxko3Gic+IMX4hYri1bWiGzEVfldWAGsAOI0VqvcvB47IG7nJ9/A6uBzcDnWuv9Dh6PxZgwiK56TsYA7YBJSqk1wG5c9HpRSr2olBqR929l4B2c6Jw4vEBJKVURWA/8BvQBogv5Rg0cgFJqjda6m1IqHPgZWAV0Rs5PjmNHV75QSk0A3ubajHYa8AzGNeMw8pIMfgB8kdXfRGAdTnJOHG7Y4eqH1AtYp7U+4ejxGFyPUqoWMhtZYRgQ58C4ZpwPZzonTmHYDQwMDAzshzP42A0MDAwM7Ihh2A0MDAzcDMOwGxgYGLgZhmE3MDAwcDMMw25gYGDgZvw/b7jx1cvPFQEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 每个月天数的分布\n",
    "for i in range(1,6):\n",
    "    plt.figure()\n",
    "    y1 = data_date[data_date.loan_month==i].loan_day.value_counts().sort_index()\n",
    "    y2 = valid_date[valid_date.loan_month==i].loan_day.value_counts().sort_index()\n",
    "    y1.plot()\n",
    "    y2.plot()\n",
    "    (y1+y2).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 统计每一列占大多数的情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 47.3 s, sys: 36 ms, total: 47.4 s\n",
      "Wall time: 47.4 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "major_dict = {col:data_raw[col].value_counts().values[0] for col in data_raw.columns}\n",
    "mvar_dict = {col:data_raw[col].value_counts().index[0] for col in data_raw.columns}\n",
    "major2_dict = {col:sum(data_raw[col].value_counts().values[:2]) for col in data_raw.columns}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['./preprocess_data_discrete/major2_dict.joblib']"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(major_dict,'./preprocess_data_discrete/major_dict.joblib')\n",
    "joblib.dump(mvar_dict,'./preprocess_data_discrete/mvar_dict.joblib')\n",
    "joblib.dump(major2_dict,'./preprocess_data_discrete/major2_dict.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "major_dict = joblib.load('./preprocess_data_discrete/major_dict.joblib')\n",
    "mvar_dict = joblib.load('./preprocess_data_discrete/mvar_dict.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f46128e5c50>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(pd.Series(major_dict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4456f59908>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(pd.Series(major2_dict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4630"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# major超过2w\n",
    "major_ser = pd.Series(major_dict)\n",
    "sum(major_ser>20000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4960"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# major2超过2w\n",
    "major2_ser = pd.Series(major2_dict)\n",
    "sum(major2_ser>20000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 拥有major值的列\n",
    "process_cols1 = list(major_ser[major_ser>=20000].index)\n",
    "# process_cols2 = list(major_ser[(major_ser<20000)&(major2_ser>=30000)].index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### major_df for train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 计算major_df\n",
    "from joblib import Parallel, delayed\n",
    "\n",
    "def p_col1(df,mvar,col): \n",
    "    # 单独计算一列\n",
    "    tmp_df = pd.DataFrame()\n",
    "    tmp_df['m'+col] = np.zeros(df.shape[0])\n",
    "    tmp_df['m'+col][df == mvar] = 1\n",
    "    tmp_df['m'+col][df == np.nan] = -1\n",
    "    return tmp_df\n",
    "\n",
    "def apply_parallel(data):\n",
    "    result = Parallel(n_jobs=32,verbose=10)(delayed(p_col1)(data[col],mvar_dict[col],col) for col in process_cols1)\n",
    "    return pd.concat(result,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=32)]: Using backend LokyBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=32)]: Done   8 tasks      | elapsed:    1.5s\n",
      "[Parallel(n_jobs=32)]: Done  21 tasks      | elapsed:    1.7s\n",
      "[Parallel(n_jobs=32)]: Done  34 tasks      | elapsed:    1.8s\n",
      "[Parallel(n_jobs=32)]: Done  49 tasks      | elapsed:    1.9s\n",
      "[Parallel(n_jobs=32)]: Done  64 tasks      | elapsed:    2.0s\n",
      "[Parallel(n_jobs=32)]: Done  81 tasks      | elapsed:    2.0s\n",
      "[Parallel(n_jobs=32)]: Done  98 tasks      | elapsed:    2.1s\n",
      "[Parallel(n_jobs=32)]: Done 117 tasks      | elapsed:    2.2s\n",
      "[Parallel(n_jobs=32)]: Done 136 tasks      | elapsed:    2.3s\n",
      "[Parallel(n_jobs=32)]: Done 157 tasks      | elapsed:    2.4s\n",
      "[Parallel(n_jobs=32)]: Done 178 tasks      | elapsed:    2.5s\n",
      "[Parallel(n_jobs=32)]: Done 201 tasks      | elapsed:    2.5s\n",
      "[Parallel(n_jobs=32)]: Done 224 tasks      | elapsed:    2.6s\n",
      "[Parallel(n_jobs=32)]: Done 249 tasks      | elapsed:    2.7s\n",
      "[Parallel(n_jobs=32)]: Done 274 tasks      | elapsed:    2.8s\n",
      "[Parallel(n_jobs=32)]: Done 301 tasks      | elapsed:    2.9s\n",
      "[Parallel(n_jobs=32)]: Done 328 tasks      | elapsed:    3.1s\n",
      "[Parallel(n_jobs=32)]: Done 357 tasks      | elapsed:    3.2s\n",
      "[Parallel(n_jobs=32)]: Done 386 tasks      | elapsed:    3.3s\n",
      "[Parallel(n_jobs=32)]: Done 417 tasks      | elapsed:    3.4s\n",
      "[Parallel(n_jobs=32)]: Done 448 tasks      | elapsed:    3.5s\n",
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      "[Parallel(n_jobs=32)]: Done 514 tasks      | elapsed:    3.8s\n",
      "[Parallel(n_jobs=32)]: Done 549 tasks      | elapsed:    3.9s\n",
      "[Parallel(n_jobs=32)]: Done 584 tasks      | elapsed:    4.1s\n",
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      "[Parallel(n_jobs=32)]: Done 736 tasks      | elapsed:    4.7s\n",
      "[Parallel(n_jobs=32)]: Done 777 tasks      | elapsed:    4.9s\n",
      "[Parallel(n_jobs=32)]: Done 818 tasks      | elapsed:    5.1s\n",
      "[Parallel(n_jobs=32)]: Done 861 tasks      | elapsed:    5.2s\n",
      "[Parallel(n_jobs=32)]: Done 904 tasks      | elapsed:    5.4s\n",
      "[Parallel(n_jobs=32)]: Done 949 tasks      | elapsed:    5.6s\n",
      "[Parallel(n_jobs=32)]: Done 994 tasks      | elapsed:    5.8s\n",
      "[Parallel(n_jobs=32)]: Done 1041 tasks      | elapsed:    6.0s\n",
      "[Parallel(n_jobs=32)]: Done 1088 tasks      | elapsed:    6.2s\n",
      "[Parallel(n_jobs=32)]: Done 1137 tasks      | elapsed:    6.4s\n",
      "[Parallel(n_jobs=32)]: Done 1186 tasks      | elapsed:    6.6s\n",
      "[Parallel(n_jobs=32)]: Done 1237 tasks      | elapsed:    6.8s\n",
      "[Parallel(n_jobs=32)]: Done 1288 tasks      | elapsed:    7.0s\n",
      "[Parallel(n_jobs=32)]: Done 1341 tasks      | elapsed:    7.2s\n",
      "[Parallel(n_jobs=32)]: Done 1394 tasks      | elapsed:    7.5s\n",
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      "[Parallel(n_jobs=32)]: Done 1504 tasks      | elapsed:    7.9s\n",
      "[Parallel(n_jobs=32)]: Done 1561 tasks      | elapsed:    8.1s\n",
      "[Parallel(n_jobs=32)]: Done 1618 tasks      | elapsed:    8.4s\n",
      "[Parallel(n_jobs=32)]: Done 1677 tasks      | elapsed:    8.6s\n",
      "[Parallel(n_jobs=32)]: Done 1736 tasks      | elapsed:    8.9s\n",
      "[Parallel(n_jobs=32)]: Done 1797 tasks      | elapsed:    9.1s\n",
      "[Parallel(n_jobs=32)]: Done 1858 tasks      | elapsed:    9.3s\n",
      "[Parallel(n_jobs=32)]: Done 1921 tasks      | elapsed:    9.6s\n",
      "[Parallel(n_jobs=32)]: Done 1984 tasks      | elapsed:    9.9s\n",
      "[Parallel(n_jobs=32)]: Done 2049 tasks      | elapsed:   10.1s\n",
      "[Parallel(n_jobs=32)]: Done 2114 tasks      | elapsed:   10.4s\n",
      "[Parallel(n_jobs=32)]: Done 2181 tasks      | elapsed:   10.6s\n",
      "[Parallel(n_jobs=32)]: Done 2248 tasks      | elapsed:   10.9s\n",
      "[Parallel(n_jobs=32)]: Done 2317 tasks      | elapsed:   11.2s\n",
      "[Parallel(n_jobs=32)]: Done 2386 tasks      | elapsed:   11.5s\n",
      "[Parallel(n_jobs=32)]: Done 2457 tasks      | elapsed:   11.8s\n",
      "[Parallel(n_jobs=32)]: Done 2528 tasks      | elapsed:   12.1s\n",
      "[Parallel(n_jobs=32)]: Done 2601 tasks      | elapsed:   12.4s\n",
      "[Parallel(n_jobs=32)]: Done 2674 tasks      | elapsed:   12.7s\n",
      "[Parallel(n_jobs=32)]: Done 2749 tasks      | elapsed:   13.0s\n",
      "[Parallel(n_jobs=32)]: Done 2824 tasks      | elapsed:   13.3s\n",
      "[Parallel(n_jobs=32)]: Done 2901 tasks      | elapsed:   13.6s\n",
      "[Parallel(n_jobs=32)]: Done 2978 tasks      | elapsed:   13.9s\n",
      "[Parallel(n_jobs=32)]: Done 3057 tasks      | elapsed:   14.2s\n",
      "[Parallel(n_jobs=32)]: Done 3136 tasks      | elapsed:   14.6s\n",
      "[Parallel(n_jobs=32)]: Done 3217 tasks      | elapsed:   14.9s\n",
      "[Parallel(n_jobs=32)]: Done 3298 tasks      | elapsed:   15.2s\n",
      "[Parallel(n_jobs=32)]: Done 3381 tasks      | elapsed:   15.5s\n",
      "[Parallel(n_jobs=32)]: Done 3464 tasks      | elapsed:   15.9s\n",
      "[Parallel(n_jobs=32)]: Done 3549 tasks      | elapsed:   16.2s\n",
      "[Parallel(n_jobs=32)]: Done 3634 tasks      | elapsed:   16.6s\n",
      "[Parallel(n_jobs=32)]: Done 3721 tasks      | elapsed:   16.9s\n",
      "[Parallel(n_jobs=32)]: Done 3808 tasks      | elapsed:   17.3s\n",
      "[Parallel(n_jobs=32)]: Done 3897 tasks      | elapsed:   17.6s\n",
      "[Parallel(n_jobs=32)]: Done 3986 tasks      | elapsed:   18.0s\n",
      "[Parallel(n_jobs=32)]: Done 4077 tasks      | elapsed:   18.4s\n",
      "[Parallel(n_jobs=32)]: Done 4168 tasks      | elapsed:   18.7s\n",
      "[Parallel(n_jobs=32)]: Done 4261 tasks      | elapsed:   19.1s\n",
      "[Parallel(n_jobs=32)]: Done 4354 tasks      | elapsed:   19.5s\n",
      "[Parallel(n_jobs=32)]: Done 4449 tasks      | elapsed:   19.9s\n",
      "[Parallel(n_jobs=32)]: Done 4544 tasks      | elapsed:   20.3s\n",
      "[Parallel(n_jobs=32)]: Done 4630 out of 4630 | elapsed:   20.6s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 18.3 s, sys: 4.58 s, total: 22.8 s\n",
      "Wall time: 23.8 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "major_df = apply_parallel(data_raw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 9.92 s, sys: 1.14 s, total: 11.1 s\n",
      "Wall time: 12.1 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['./preprocess_data_discrete/major_df.lz4']"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "joblib.dump(major_df, './preprocess_data_discrete/major_df.lz4', compress='lz4')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### major count for train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib\n",
    "major_df = joblib.load('./preprocess_data_discrete/major_df.lz4')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 26.8 s, sys: 1.08 s, total: 27.9 s\n",
      "Wall time: 27.9 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "maj_cnt_df = pd.DataFrame()\n",
    "maj_cnt_df['maj_cnt'] = (major_df == 1).sum(axis=1).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "maj_cnt_df['maj_cntn'] = pd.cut(maj_cnt_df['maj_cnt'],bins=10,labels=range(1,11)).astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8     49031\n",
       "7     25736\n",
       "9     19725\n",
       "6      4310\n",
       "10      776\n",
       "5       264\n",
       "2       102\n",
       "1        42\n",
       "3        10\n",
       "4         4\n",
       "Name: maj_cntn, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maj_cnt_df.maj_cntn.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['./preprocess_data_discrete/maj_cnt_df.lz4']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import joblib\n",
    "joblib.dump(maj_cnt_df,'./preprocess_data_discrete/maj_cnt_df.lz4')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### major count for test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib\n",
    "major_test = joblib.load('./preprocess_data_discrete/major_test.lz4')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 7.02 s, sys: 480 ms, total: 7.5 s\n",
      "Wall time: 7.5 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "maj_cnt_test = pd.DataFrame()\n",
    "maj_cnt_test['maj_cnt'] = (major_test == 1).sum(axis=1).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "maj_cnt_test['maj_cntn'] = pd.cut(maj_cnt_test['maj_cnt'],bins=10,labels=range(1,11)).astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8     9597\n",
       "9     5499\n",
       "7     3973\n",
       "6      550\n",
       "10     324\n",
       "5       27\n",
       "2       22\n",
       "1        5\n",
       "3        3\n",
       "Name: maj_cntn, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maj_cnt_test.maj_cntn.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['./preprocess_data_discrete/maj_cnt_test.lz4']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import joblib\n",
    "joblib.dump(maj_cnt_test,'./preprocess_data_discrete/maj_cnt_test.lz4')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### major_test for test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=32)]: Using backend LokyBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=32)]: Done   8 tasks      | elapsed:    1.3s\n",
      "[Parallel(n_jobs=32)]: Done  21 tasks      | elapsed:    1.4s\n",
      "[Parallel(n_jobs=32)]: Done  34 tasks      | elapsed:    1.4s\n",
      "[Parallel(n_jobs=32)]: Done  49 tasks      | elapsed:    1.5s\n",
      "[Parallel(n_jobs=32)]: Done  64 tasks      | elapsed:    1.5s\n",
      "[Parallel(n_jobs=32)]: Batch computation too fast (0.1821s.) Setting batch_size=2.\n",
      "[Parallel(n_jobs=32)]: Done  81 tasks      | elapsed:    1.5s\n",
      "[Parallel(n_jobs=32)]: Done  98 tasks      | elapsed:    1.6s\n",
      "[Parallel(n_jobs=32)]: Done 117 tasks      | elapsed:    1.6s\n",
      "[Parallel(n_jobs=32)]: Batch computation too fast (0.1476s.) Setting batch_size=4.\n",
      "[Parallel(n_jobs=32)]: Done 142 tasks      | elapsed:    1.7s\n",
      "[Parallel(n_jobs=32)]: Done 183 tasks      | elapsed:    1.7s\n",
      "[Parallel(n_jobs=32)]: Done 224 tasks      | elapsed:    1.8s\n",
      "[Parallel(n_jobs=32)]: Batch computation too fast (0.1865s.) Setting batch_size=8.\n",
      "[Parallel(n_jobs=32)]: Done 292 tasks      | elapsed:    1.9s\n",
      "[Parallel(n_jobs=32)]: Done 384 tasks      | elapsed:    2.0s\n",
      "[Parallel(n_jobs=32)]: Done 484 tasks      | elapsed:    2.1s\n",
      "[Parallel(n_jobs=32)]: Done 664 tasks      | elapsed:    2.4s\n",
      "[Parallel(n_jobs=32)]: Done 880 tasks      | elapsed:    2.6s\n",
      "[Parallel(n_jobs=32)]: Done 1096 tasks      | elapsed:    2.9s\n",
      "[Parallel(n_jobs=32)]: Done 1328 tasks      | elapsed:    3.2s\n",
      "[Parallel(n_jobs=32)]: Done 1560 tasks      | elapsed:    3.4s\n",
      "[Parallel(n_jobs=32)]: Done 1808 tasks      | elapsed:    3.8s\n",
      "[Parallel(n_jobs=32)]: Done 2056 tasks      | elapsed:    4.1s\n",
      "[Parallel(n_jobs=32)]: Done 2320 tasks      | elapsed:    4.4s\n",
      "[Parallel(n_jobs=32)]: Done 2584 tasks      | elapsed:    4.7s\n",
      "[Parallel(n_jobs=32)]: Done 2864 tasks      | elapsed:    5.0s\n",
      "[Parallel(n_jobs=32)]: Done 3144 tasks      | elapsed:    5.3s\n",
      "[Parallel(n_jobs=32)]: Done 3440 tasks      | elapsed:    5.7s\n",
      "[Parallel(n_jobs=32)]: Done 3736 tasks      | elapsed:    6.0s\n",
      "[Parallel(n_jobs=32)]: Done 4048 tasks      | elapsed:    6.4s\n",
      "[Parallel(n_jobs=32)]: Done 4630 out of 4630 | elapsed:    7.0s finished\n"
     ]
    }
   ],
   "source": [
    "major_test = apply_parallel(data_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3.66 s, sys: 488 ms, total: 4.14 s\n",
      "Wall time: 4.94 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['./preprocess_data_discrete/major_test.lz4']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "joblib.dump(major_test, './preprocess_data_discrete/major_test.lz4', compress='lz4')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 查看uniq_num的分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_raw_new = data_raw.fillna(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 9.64 s, sys: 4 ms, total: 9.64 s\n",
      "Wall time: 9.64 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "uniq = [[col,data_raw_new[col].unique().shape[0]] for col in data_raw.columns]\n",
    "uniq_ser = pd.Series(index=np.array(uniq)[:,0],data=np.array(uniq)[:,1].astype(int))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f460dfb4240>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(uniq_ser.values,bins=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f45f452d668>"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.array(result)[:,0])\n",
    "plt.plot(np.array(result)[:,1])\n",
    "plt.plot(np.array(result)[:,2])\n",
    "plt.legend(['cat_cols','dcont_cols','scont_cols'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "def look_trend(data,uniq,thre = 20):\n",
    "#     uniq_sort = sorted(uniq,key=lambda x: x[1],reverse=False)\n",
    "    cat_cols = [col for col,num in uniq if num>1 and num<=thre and data[col].dtype==np.int64]\n",
    "    dcont_cols = [col for col,num in uniq if num>1 and num<=thre and data[col].dtype!=np.int64]\n",
    "    scont_cols = [col for col,num in uniq if num>1 and num>thre]\n",
    "#     print(len(cat_cols),len(dcont_cols),len(scont_cols))\n",
    "    return len(cat_cols), len(dcont_cols),len(scont_cols)\n",
    "result = [look_trend(data_raw_new,uniq,i) for i in range(1,5000)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.小于10, cat\n",
    "### 2.10~400, 离散化\n",
    "### 3.400~... , 标准化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 分离 离散型特征 和 连续型特征\n",
    "def cat_and_cont(uniq,thre1 = 10, thre2 = 400):\n",
    "#     uniq_sort = sorted(uniq,key=lambda x: x[1],reverse=False)\n",
    "    cat_cols = [col for col,num in uniq if num>=1 and num<=thre1 ]\n",
    "    dcont_cols = [col for col,num in uniq if num>thre1 and num<=thre2 ]\n",
    "    scont_cols = [col for col,num in uniq if num>thre2]\n",
    "    print(len(cat_cols),len(dcont_cols),len(scont_cols))\n",
    "    return cat_cols, dcont_cols,scont_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "901 2610 3186\n"
     ]
    }
   ],
   "source": [
    "cat_cols, dcont_cols,scont_cols = cat_and_cont(uniq,10,400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['./preprocess_data_discrete/three_tuple.joblib']"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump((cat_cols, dcont_cols,scont_cols),'./preprocess_data_discrete/three_tuple.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_cols, dcont_cols,scont_cols = joblib.load('./preprocess_data_discrete/three_tuple.joblib')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### cat_cols for train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.27 s, sys: 276 ms, total: 1.54 s\n",
      "Wall time: 1.54 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# cat_cols保留\n",
    "cat_df = data_raw[cat_cols]\n",
    "joblib.dump(cat_df,'./preprocess_data_discrete/cat_df.lz4', compress='lz4')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### cat_cols for test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 272 ms, sys: 116 ms, total: 388 ms\n",
      "Wall time: 388 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# cat_cols保留\n",
    "cat_test = data_test[cat_cols]\n",
    "joblib.dump(cat_test,'./preprocess_data_discrete/cat_test.lz4', compress='lz4')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### dcont_cols for train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dcont_cols离散化\n",
    "from joblib import Parallel, delayed\n",
    "\n",
    "def p_col2(ser,col): \n",
    "    # 单独计算一列\n",
    "    tmp_df = pd.DataFrame()\n",
    "    tmp_df[col] = pd.cut(ser,bins=10,labels=range(1,11)).astype('int')\n",
    "    return tmp_df\n",
    "def discrete_parallel(data):\n",
    "    result = Parallel(n_jobs=32,verbose=10)(delayed(p_col2)(data[col],col) for col in dcont_cols)\n",
    "    return pd.concat(result,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=32)]: Using backend LokyBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=32)]: Done   8 tasks      | elapsed:    2.1s\n",
      "[Parallel(n_jobs=32)]: Done  21 tasks      | elapsed:    2.4s\n",
      "[Parallel(n_jobs=32)]: Done  34 tasks      | elapsed:    2.6s\n",
      "[Parallel(n_jobs=32)]: Done  49 tasks      | elapsed:    2.8s\n",
      "[Parallel(n_jobs=32)]: Done  64 tasks      | elapsed:    3.0s\n",
      "[Parallel(n_jobs=32)]: Done  81 tasks      | elapsed:    3.2s\n",
      "[Parallel(n_jobs=32)]: Done  98 tasks      | elapsed:    3.3s\n",
      "[Parallel(n_jobs=32)]: Done 117 tasks      | elapsed:    3.5s\n",
      "[Parallel(n_jobs=32)]: Done 136 tasks      | elapsed:    3.7s\n",
      "[Parallel(n_jobs=32)]: Done 157 tasks      | elapsed:    4.0s\n",
      "[Parallel(n_jobs=32)]: Done 178 tasks      | elapsed:    4.2s\n",
      "[Parallel(n_jobs=32)]: Done 201 tasks      | elapsed:    4.3s\n",
      "[Parallel(n_jobs=32)]: Done 224 tasks      | elapsed:    4.5s\n",
      "[Parallel(n_jobs=32)]: Done 249 tasks      | elapsed:    4.6s\n",
      "[Parallel(n_jobs=32)]: Done 274 tasks      | elapsed:    4.8s\n",
      "[Parallel(n_jobs=32)]: Done 301 tasks      | elapsed:    5.0s\n",
      "[Parallel(n_jobs=32)]: Done 328 tasks      | elapsed:    5.2s\n",
      "[Parallel(n_jobs=32)]: Done 357 tasks      | elapsed:    5.4s\n",
      "[Parallel(n_jobs=32)]: Done 386 tasks      | elapsed:    5.6s\n",
      "[Parallel(n_jobs=32)]: Done 417 tasks      | elapsed:    5.8s\n",
      "[Parallel(n_jobs=32)]: Done 448 tasks      | elapsed:    6.1s\n",
      "[Parallel(n_jobs=32)]: Done 481 tasks      | elapsed:    6.3s\n",
      "[Parallel(n_jobs=32)]: Done 514 tasks      | elapsed:    6.6s\n",
      "[Parallel(n_jobs=32)]: Done 549 tasks      | elapsed:    6.7s\n",
      "[Parallel(n_jobs=32)]: Done 584 tasks      | elapsed:    7.0s\n",
      "[Parallel(n_jobs=32)]: Done 621 tasks      | elapsed:    7.2s\n",
      "[Parallel(n_jobs=32)]: Done 658 tasks      | elapsed:    7.6s\n",
      "[Parallel(n_jobs=32)]: Done 697 tasks      | elapsed:    8.0s\n",
      "[Parallel(n_jobs=32)]: Done 736 tasks      | elapsed:    8.3s\n",
      "[Parallel(n_jobs=32)]: Done 777 tasks      | elapsed:    8.7s\n",
      "[Parallel(n_jobs=32)]: Done 818 tasks      | elapsed:    9.1s\n",
      "[Parallel(n_jobs=32)]: Done 861 tasks      | elapsed:    9.5s\n",
      "[Parallel(n_jobs=32)]: Done 904 tasks      | elapsed:    9.9s\n",
      "[Parallel(n_jobs=32)]: Done 949 tasks      | elapsed:   10.4s\n",
      "[Parallel(n_jobs=32)]: Done 994 tasks      | elapsed:   10.7s\n",
      "[Parallel(n_jobs=32)]: Done 1041 tasks      | elapsed:   11.1s\n",
      "[Parallel(n_jobs=32)]: Done 1088 tasks      | elapsed:   11.6s\n",
      "[Parallel(n_jobs=32)]: Done 1137 tasks      | elapsed:   12.0s\n",
      "[Parallel(n_jobs=32)]: Done 1186 tasks      | elapsed:   12.4s\n",
      "[Parallel(n_jobs=32)]: Done 1237 tasks      | elapsed:   12.8s\n",
      "[Parallel(n_jobs=32)]: Done 1288 tasks      | elapsed:   13.2s\n",
      "[Parallel(n_jobs=32)]: Done 1341 tasks      | elapsed:   13.6s\n",
      "[Parallel(n_jobs=32)]: Done 1394 tasks      | elapsed:   14.1s\n",
      "[Parallel(n_jobs=32)]: Done 1449 tasks      | elapsed:   14.5s\n",
      "[Parallel(n_jobs=32)]: Done 1504 tasks      | elapsed:   14.9s\n",
      "[Parallel(n_jobs=32)]: Done 1561 tasks      | elapsed:   15.4s\n",
      "[Parallel(n_jobs=32)]: Done 1618 tasks      | elapsed:   15.9s\n",
      "[Parallel(n_jobs=32)]: Done 1677 tasks      | elapsed:   16.3s\n",
      "[Parallel(n_jobs=32)]: Done 1736 tasks      | elapsed:   16.7s\n",
      "[Parallel(n_jobs=32)]: Done 1797 tasks      | elapsed:   17.2s\n",
      "[Parallel(n_jobs=32)]: Done 1858 tasks      | elapsed:   17.6s\n",
      "[Parallel(n_jobs=32)]: Done 1921 tasks      | elapsed:   18.0s\n",
      "[Parallel(n_jobs=32)]: Done 1984 tasks      | elapsed:   18.5s\n",
      "[Parallel(n_jobs=32)]: Done 2049 tasks      | elapsed:   18.9s\n",
      "[Parallel(n_jobs=32)]: Done 2114 tasks      | elapsed:   19.5s\n",
      "[Parallel(n_jobs=32)]: Done 2181 tasks      | elapsed:   20.3s\n",
      "[Parallel(n_jobs=32)]: Done 2248 tasks      | elapsed:   20.7s\n",
      "[Parallel(n_jobs=32)]: Done 2317 tasks      | elapsed:   21.2s\n",
      "[Parallel(n_jobs=32)]: Done 2386 tasks      | elapsed:   22.0s\n",
      "[Parallel(n_jobs=32)]: Done 2457 tasks      | elapsed:   22.6s\n",
      "[Parallel(n_jobs=32)]: Done 2528 tasks      | elapsed:   23.2s\n",
      "[Parallel(n_jobs=32)]: Done 2610 out of 2610 | elapsed:   23.9s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 8.59 s, sys: 8.79 s, total: 17.4 s\n",
      "Wall time: 26.5 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "discrete_df = discrete_parallel(data_raw)\n",
    "discrete_df.columns = ['d'+col for col in discrete_df.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['./preprocess_data_discrete/discrete_df.lz4']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "joblib.dump(discrete_df,'./preprocess_data_discrete/discrete_df.lz4', compress='lz4')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### dcont_cols for test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=32)]: Using backend LokyBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=32)]: Batch computation too fast (0.0287s.) Setting batch_size=12.\n",
      "[Parallel(n_jobs=32)]: Done   8 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=32)]: Done  21 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=32)]: Done  34 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=32)]: Done  49 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=32)]: Done  64 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=32)]: Done 268 tasks      | elapsed:    0.5s\n",
      "[Parallel(n_jobs=32)]: Done 472 tasks      | elapsed:    0.8s\n",
      "[Parallel(n_jobs=32)]: Done 700 tasks      | elapsed:    1.1s\n",
      "[Parallel(n_jobs=32)]: Done 928 tasks      | elapsed:    1.4s\n",
      "[Parallel(n_jobs=32)]: Done 1180 tasks      | elapsed:    1.7s\n",
      "[Parallel(n_jobs=32)]: Done 1432 tasks      | elapsed:    2.0s\n",
      "[Parallel(n_jobs=32)]: Done 1708 tasks      | elapsed:    2.4s\n",
      "[Parallel(n_jobs=32)]: Done 2610 out of 2610 | elapsed:    3.5s finished\n"
     ]
    }
   ],
   "source": [
    "discrete_test = discrete_parallel(data_test)\n",
    "discrete_test.columns = ['d'+col for col in discrete_test.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2.07 s, sys: 76 ms, total: 2.14 s\n",
      "Wall time: 2.14 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['./preprocess_data_discrete/discrete_test.lz4']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "joblib.dump(discrete_test,'./preprocess_data_discrete/discrete_test.lz4', compress='lz4')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### scont_cols for train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# scont_cols标准化\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler(with_mean=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def p_col3(ser,col): \n",
    "    # 单独计算一列\n",
    "    tmp_df = pd.DataFrame()\n",
    "    tmp = scaler.fit_transform(ser.values.reshape(-1,1))\n",
    "    tmp_df[col] = tmp.ravel()\n",
    "    return tmp_df\n",
    "def scale_parallel(data):\n",
    "    result = Parallel(n_jobs=32,verbose=10)(delayed(p_col3)(data[col],col) for col in scont_cols)\n",
    "    return pd.concat(result,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=32)]: Using backend LokyBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=32)]: Done   8 tasks      | elapsed:    0.4s\n",
      "[Parallel(n_jobs=32)]: Done  21 tasks      | elapsed:    0.5s\n",
      "[Parallel(n_jobs=32)]: Done  34 tasks      | elapsed:    0.5s\n",
      "[Parallel(n_jobs=32)]: Done  49 tasks      | elapsed:    0.6s\n",
      "[Parallel(n_jobs=32)]: Done  64 tasks      | elapsed:    0.6s\n",
      "[Parallel(n_jobs=32)]: Done  81 tasks      | elapsed:    0.7s\n",
      "[Parallel(n_jobs=32)]: Done  98 tasks      | elapsed:    0.7s\n",
      "[Parallel(n_jobs=32)]: Batch computation too fast (0.1996s.) Setting batch_size=2.\n",
      "[Parallel(n_jobs=32)]: Done 117 tasks      | elapsed:    0.8s\n",
      "[Parallel(n_jobs=32)]: Done 136 tasks      | elapsed:    0.8s\n",
      "[Parallel(n_jobs=32)]: Done 157 tasks      | elapsed:    0.9s\n",
      "[Parallel(n_jobs=32)]: Batch computation too fast (0.1725s.) Setting batch_size=4.\n",
      "[Parallel(n_jobs=32)]: Done 188 tasks      | elapsed:    1.0s\n",
      "[Parallel(n_jobs=32)]: Done 234 tasks      | elapsed:    1.1s\n",
      "[Parallel(n_jobs=32)]: Done 280 tasks      | elapsed:    1.2s\n",
      "[Parallel(n_jobs=32)]: Done 364 tasks      | elapsed:    1.5s\n",
      "[Parallel(n_jobs=32)]: Done 464 tasks      | elapsed:    1.8s\n",
      "[Parallel(n_jobs=32)]: Done 572 tasks      | elapsed:    2.5s\n",
      "[Parallel(n_jobs=32)]: Done 680 tasks      | elapsed:    2.9s\n",
      "[Parallel(n_jobs=32)]: Done 796 tasks      | elapsed:    3.2s\n",
      "[Parallel(n_jobs=32)]: Done 912 tasks      | elapsed:    3.5s\n",
      "[Parallel(n_jobs=32)]: Done 1036 tasks      | elapsed:    3.9s\n",
      "[Parallel(n_jobs=32)]: Done 1160 tasks      | elapsed:    4.3s\n",
      "[Parallel(n_jobs=32)]: Done 1292 tasks      | elapsed:    4.7s\n",
      "[Parallel(n_jobs=32)]: Done 1424 tasks      | elapsed:    5.1s\n",
      "[Parallel(n_jobs=32)]: Done 1564 tasks      | elapsed:    5.6s\n",
      "[Parallel(n_jobs=32)]: Done 1704 tasks      | elapsed:    6.0s\n",
      "[Parallel(n_jobs=32)]: Done 1852 tasks      | elapsed:    6.5s\n",
      "[Parallel(n_jobs=32)]: Done 2000 tasks      | elapsed:    6.9s\n",
      "[Parallel(n_jobs=32)]: Done 2156 tasks      | elapsed:    7.4s\n",
      "[Parallel(n_jobs=32)]: Done 2312 tasks      | elapsed:    8.0s\n",
      "[Parallel(n_jobs=32)]: Done 2476 tasks      | elapsed:    8.5s\n",
      "[Parallel(n_jobs=32)]: Done 2640 tasks      | elapsed:    9.0s\n",
      "[Parallel(n_jobs=32)]: Done 2812 tasks      | elapsed:    9.5s\n",
      "[Parallel(n_jobs=32)]: Done 3186 out of 3186 | elapsed:   10.6s finished\n"
     ]
    }
   ],
   "source": [
    "scale_df = scale_parallel(data_raw)\n",
    "scale_df.columns = ['s'+col for col in scale_df.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 13.3 s, sys: 1.84 s, total: 15.1 s\n",
      "Wall time: 28.9 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['./preprocess_data_discrete/scale_df.lz4']"
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "joblib.dump(scale_df, './preprocess_data_discrete/scale_df.lz4', compress='lz4')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### scont_cols for test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=32)]: Using backend LokyBackend with 32 concurrent workers.\n",
      "[Parallel(n_jobs=32)]: Done   8 tasks      | elapsed:    0.4s\n",
      "[Parallel(n_jobs=32)]: Done  21 tasks      | elapsed:    0.4s\n",
      "[Parallel(n_jobs=32)]: Done  34 tasks      | elapsed:    0.4s\n",
      "[Parallel(n_jobs=32)]: Done  49 tasks      | elapsed:    0.5s\n",
      "[Parallel(n_jobs=32)]: Batch computation too fast (0.1802s.) Setting batch_size=2.\n",
      "[Parallel(n_jobs=32)]: Done  64 tasks      | elapsed:    0.5s\n",
      "[Parallel(n_jobs=32)]: Done  81 tasks      | elapsed:    0.5s\n",
      "[Parallel(n_jobs=32)]: Done  98 tasks      | elapsed:    0.5s\n",
      "[Parallel(n_jobs=32)]: Batch computation too fast (0.1022s.) Setting batch_size=6.\n",
      "[Parallel(n_jobs=32)]: Done 120 tasks      | elapsed:    0.6s\n",
      "[Parallel(n_jobs=32)]: Done 157 tasks      | elapsed:    0.6s\n",
      "[Parallel(n_jobs=32)]: Done 199 tasks      | elapsed:    0.7s\n",
      "[Parallel(n_jobs=32)]: Done 245 tasks      | elapsed:    0.7s\n",
      "[Parallel(n_jobs=32)]: Batch computation too fast (0.1588s.) Setting batch_size=14.\n",
      "[Parallel(n_jobs=32)]: Done 383 tasks      | elapsed:    0.9s\n",
      "[Parallel(n_jobs=32)]: Done 521 tasks      | elapsed:    1.0s\n",
      "[Parallel(n_jobs=32)]: Done 735 tasks      | elapsed:    1.2s\n",
      "[Parallel(n_jobs=32)]: Done 1085 tasks      | elapsed:    1.6s\n",
      "[Parallel(n_jobs=32)]: Done 1463 tasks      | elapsed:    1.9s\n",
      "[Parallel(n_jobs=32)]: Done 1841 tasks      | elapsed:    2.3s\n",
      "[Parallel(n_jobs=32)]: Done 2247 tasks      | elapsed:    2.6s\n",
      "[Parallel(n_jobs=32)]: Done 2485 out of 3186 | elapsed:    3.0s remaining:    0.8s\n",
      "[Parallel(n_jobs=32)]: Done 3186 out of 3186 | elapsed:    3.6s finished\n"
     ]
    }
   ],
   "source": [
    "scale_test = scale_parallel(data_test)\n",
    "scale_test.columns = ['s'+col for col in scale_test.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3.84 s, sys: 440 ms, total: 4.28 s\n",
      "Wall time: 4.28 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['./preprocess_data_discrete/scale_test.lz4']"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "joblib.dump(scale_test, './preprocess_data_discrete/scale_test.lz4', compress='lz4')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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